Super_Resolution
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AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution
2026-02-28 10:39:06Cencen Liu (University of Electronic Science and Technology of China), Dongyang Zhang (University of Electronic Science and Technology of China, Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province), Wen Yin (University of Electronic Science and Technology of China), Jielei Wang (University of Electronic Science and Technology of China, Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province), Tianyu Li (University of Electronic Science and Technology of China), Ji Guo (University of Electronic Science and Technology of China), Wenbo Jiang (University of Electronic Science and Technology of China), Guoqing Wang (University of Electronic Science and Technology of China), Guoming Lu (University of Electronic Science and Technology of China, Ubiquitous Intelligence and Trusted Services Key Laboratory of Sichuan Province)Abstract
Visual autoregressive (VAR) models have recently emerged as a promising alternative for image generation, offering stable training, non-iterative inference, and high-fidelity synthesis through next-scale prediction. This encourages the exploration of VAR for image super-resolution (ISR), yet its application remains underexplored and faces two critical challenges: locality-biased attention, which fragments spatial structures, and residual-only supervision, which accumulates errors across scales, severely compromises global consistency of reconstructed images. To address these issues, we propose AlignVAR, a globally consistent visual autoregressive framework tailored for ISR, featuring two key components: (1) Spatial Consistency Autoregression (SCA), which applies an adaptive mask to reweight attention toward structurally correlated regions, thereby mitigating excessive locality and enhancing long-range dependencies; and (2) Hierarchical Consistency Constraint (HCC), which augments residual learning with full reconstruction supervision at each scale, exposing accumulated deviations early and stabilizing the coarse-to-fine refinement process. Extensive experiments demonstrate that AlignVAR consistently enhances structural coherence and perceptual fidelity over existing generative methods, while delivering over 10x faster inference with nearly 50% fewer parameters than leading diffusion-based approaches, establishing a new paradigm for efficient ISR.
Abstract (translated)
最近，视觉自回归（VAR）模型作为图像生成的一个有前景的替代方案出现，通过下一尺度预测提供了稳定的训练、非迭代推理和高保真合成。这激发了将VAR应用于超分辨率图像恢复（ISR）的研究兴趣，尽管其应用仍然有限，并面临两大关键挑战：局部偏置注意机制，它会破坏空间结构；以及仅限残差监督，这会导致跨尺度累积误差，严重损害重建图像的整体一致性。为了解决这些问题，我们提出了AlignVAR，这是一种专为ISR设计的全局一致视觉自回归框架，具有两个主要组件： 1. **空间一致性自回归（SCA）**：该机制应用了一个自适应掩码来重新加权注意权重以偏重于结构相关的区域，从而缓解过度局部化的现象，并增强长距离依赖关系。 2. **层级一致性约束（HCC）**：该方法通过在每个尺度上添加完全重建监督来补充残差学习，这有助于及早暴露累积的偏差并稳定从粗到细的细化过程。 广泛的实验表明，与现有的生成方法相比，AlignVAR在结构一致性和感知保真度方面始终具有显著优势。此外，它还提供了超过10倍更快的推理速度，并且参数量比领先的基于扩散的方法减少了近50%，从而为高效ISR建立了一种新的范式。
URL
https://arxiv.org/abs/2603.00589
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Improved Adversarial Diffusion Compression for Real-World Video Super-Resolution
2026-02-28 04:30:54Bin Chen, Weiqi Li, Shijie Zhao, Xuanyu Zhang, Junlin Li, Li Zhang, Jian ZhangAbstract
While many diffusion models have achieved impressive results in real-world video super-resolution (Real-VSR) by generating rich and realistic details, their reliance on multi-step sampling leads to slow inference. One-step networks like SeedVR2, DOVE, and DLoRAL alleviate this through condensing generation into one single step, yet they remain heavy, with billions of parameters and multi-second latency. Recent adversarial diffusion compression (ADC) offers a promising path via pruning and distilling these models into a compact AdcSR network, but directly applying it to Real-VSR fails to balance spatial details and temporal consistency due to its lack of temporal awareness and the limitations of standard adversarial learning. To address these challenges, we propose an improved ADC method for Real-VSR. Our approach distills a large diffusion Transformer (DiT) teacher DOVE equipped with 3D spatio-temporal attentions, into a pruned 2D Stable Diffusion (SD)-based AdcSR backbone, augmented with lightweight 1D temporal convolutions, achieving significantly higher efficiency. In addition, we introduce a dual-head adversarial distillation scheme, in which discriminators in both pixel and feature domains explicitly disentangle the discrimination of details and consistency into two heads, enabling both objectives to be effectively optimized without sacrificing one for the other. Experiments demonstrate that the resulting compressed AdcVSR model reduces complexity by 95% in parameters and achieves an 8$\times$ acceleration over its DiT teacher DOVE, while maintaining competitive video quality and efficiency.
Abstract (translated)
虽然许多扩散模型在现实世界的视频超分辨率（Real-VSR）任务中通过生成丰富和逼真的细节取得了令人印象深刻的结果，但它们依赖于多步骤采样导致推理速度慢。类似于SeedVR2、DOVE和DLoRAL的一步网络通过将生成过程压缩为单一步骤来缓解这一问题，但仍保持庞大体量，拥有数十亿参数且存在数秒延迟的问题。最近的对抗性扩散压缩（ADC）方法通过修剪并蒸馏这些模型至紧凑型AdcSR网络提供了一种有希望的道路，但直接应用于Real-VSR时由于缺乏时间感知能力及标准对抗学习的限制而无法平衡空间细节与时间一致性。 为了解决这些问题，我们提出了一种改进的ADC方法用于Real-VSR。我们的方法从一个大型扩散Transformer（DiT）教师网络DOVE开始，该网络配备了3D时空注意力机制，并将它蒸馏至一个轻量级修剪后的2D稳定扩散（SD）基础AdcSR模型上，同时增加了轻量的一维时间卷积，从而显著提高了效率。此外，我们引入了一个双头对抗性蒸馏方案，在像素域和特征域中明确区分细节与一致性的鉴别器被拆分为两个头部，使得两者都能在不牺牲其中一个目标的情况下有效优化。 实验表明，压缩后的AdcVSR模型相较于其DiT教师DOVE在参数复杂度上减少了95%，实现了8倍的加速，同时保持了竞争性的视频质量和效率。
URL
https://arxiv.org/abs/2603.00458
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Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution
2026-02-27 18:13:31Chengyan Deng, Zhangquan Chen, Li Yu, Kai Zhang, Xue Zhou, Wang ZhangAbstract
Diffusion-based Real-World Image Super-Resolution (Real-ISR) achieves impressive perceptual quality but suffers from high computational costs due to iterative sampling. While recent distillation approaches leveraging large-scale Text-to-Image (T2I) priors have enabled one-step generation, they are typically hindered by prohibitive parameter counts and the inherent capability bounds imposed by teacher models. As a lightweight alternative, Consistency Models offer efficient inference but struggle with two critical limitations: the accumulation of consistency drift inherent to transitive training, and a phenomenon we term "Geometric Decoupling" - where the generative trajectory achieves pixel-wise alignment yet fails to preserve structural coherence. To address these challenges, we propose GTASR (Geometric Trajectory Alignment Super-Resolution), a simple yet effective consistency training paradigm for Real-ISR. Specifically, we introduce a Trajectory Alignment (TA) strategy to rectify the tangent vector field via full-path projection, and a Dual-Reference Structural Rectification (DRSR) mechanism to enforce strict structural constraints. Extensive experiments verify that GTASR delivers superior performance over representative baselines while maintaining minimal latency. The code and model will be released at this https URL.
Abstract (translated)
基于扩散的现实世界图像超分辨率（Real-ISR）在感知质量方面表现出色，但由于迭代采样导致计算成本高昂。尽管最近利用大规模文本到图像（T2I）先验知识的蒸馏方法已实现了一步式生成，但这些方法通常受限于庞大的参数数量和教师模型内在的能力限制。作为轻量级替代方案，一致性模型提供了高效的推理，但存在两个关键问题：由于传递训练固有的连贯性漂移累积以及我们称之为“几何解耦”的现象——即生成轨迹在像素级别上对齐但仍无法保持结构一致性。 为解决这些问题，我们提出了GTASR（几何轨迹对准超分辨率），这是一种简单而有效的现实世界图像超分辨的一致性训练范式。具体来说，我们引入了轨迹对准(TA)策略，通过全路径投影修正切向矢量场，并提出了一种双参考结构校正(DRSR)机制以强制严格的结构约束。 广泛的实验验证显示，GTASR在代表性的基线模型上提供了优越的性能并保持最低延迟。代码和模型将在[此链接](https://this https URL/)发布。
URL
https://arxiv.org/abs/2602.24240
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SR3R: Rethinking Super-Resolution 3D Reconstruction With Feed-Forward Gaussian Splatting
2026-02-27 13:45:22Xiang Feng, Xiangbo Wang, Tieshi Zhong, Chengkai Wang, Yiting Zhao, Tianxiang Xu, Zhenzhong Kuang, Feiwei Qin, Xuefei Yin, Yanming ZhuAbstract
3D super-resolution (3DSR) aims to reconstruct high-resolution (HR) 3D scenes from low-resolution (LR) multi-view images. Existing methods rely on dense LR inputs and per-scene optimization, which restricts the high-frequency priors for constructing HR 3D Gaussian Splatting (3DGS) to those inherited from pretrained 2D super-resolution (2DSR) models. This severely limits reconstruction fidelity, cross-scene generalization, and real-time usability. We propose to reformulate 3DSR as a direct feed-forward mapping from sparse LR views to HR 3DGS representations, enabling the model to autonomously learn 3D-specific high-frequency geometry and appearance from large-scale, multi-scene data. This fundamentally changes how 3DSR acquires high-frequency knowledge and enables robust generalization to unseen scenes. Specifically, we introduce SR3R, a feed-forward framework that directly predicts HR 3DGS representations from sparse LR views via the learned mapping network. To further enhance reconstruction fidelity, we introduce Gaussian offset learning and feature refinement, which stabilize reconstruction and sharpen high-frequency details. SR3R is plug-and-play and can be paired with any feed-forward 3DGS reconstruction backbone: the backbone provides an LR 3DGS scaffold, and SR3R upscales it to an HR 3DGS. Extensive experiments across three 3D benchmarks demonstrate that SR3R surpasses state-of-the-art (SOTA) 3DSR methods and achieves strong zero-shot generalization, even outperforming SOTA per-scene optimization methods on unseen scenes.
Abstract (translated)
3D超分辨率（3DSR）的目标是从低分辨率（LR）的多视角图像中重建高分辨率（HR）的3D场景。现有的方法依赖于密集的低分辨率输入和针对每个场景进行优化，这限制了构建高分辨率3D高斯点集（3DGS）时只能使用从预训练的2D超分辨率（2DSR）模型继承来的高频先验知识。这种情况严重限制了重建的保真度、跨场景泛化能力以及实时可用性。 我们提出了一种将3DSR重新构想为一种直接从前向馈送稀疏低分辨率视图到高分辨率3DGS表示的方法，这种方法使模型能够自主地从大规模多场景数据中学习特定于3D的高频几何和外观特征。这从根本上改变了获取高频知识的方式，并且使得对未见过的场景进行稳健泛化成为可能。 具体而言，我们引入了SR3R，这是一种前向馈送框架，通过所学映射网络直接从前向馈送稀疏低分辨率视图预测高分辨率3DGS表示。为了进一步提高重建保真度，我们提出了高斯偏移学习和特征细化技术，这些技术稳定了重建过程，并增强了高频细节。 SR3R是一种即插即用的框架，可以与任何前向馈送3DGS重建骨干网配合使用：该骨架提供低分辨率的3DGS基础结构，而SR3R将其提升到高分辨率的3DGS。在三个3D基准测试上的广泛实验表明，SR3R超过了最先进的（SOTA）3DSR方法，并实现了强大的零样本泛化性能，在未见过场景中甚至超越了最先进的针对每个场景优化的方法。
URL
https://arxiv.org/abs/2602.24020
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DACESR: Degradation-Aware Conditional Embedding for Real-World Image Super-Resolution
2026-02-27 10:29:50Xiaoyan Lei, Wenlong Zhang, Biao Luo, Hui Liang, Weifeng Cao, Qiuting LinAbstract
Multimodal large models have shown excellent ability in addressing image super-resolution in real-world scenarios by leveraging language class as condition information, yet their abilities in degraded images remain limited. In this paper, we first revisit the capabilities of the Recognize Anything Model (RAM) for degraded images by calculating text similarity. We find that directly using contrastive learning to fine-tune RAM in the degraded space is difficult to achieve acceptable results. To address this issue, we employ a degradation selection strategy to propose a Real Embedding Extractor (REE), which achieves significant recognition performance gain on degraded image content through contrastive learning. Furthermore, we use a Conditional Feature Modulator (CFM) to incorporate the high-level information of REE for a powerful Mamba-based network, which can leverage effective pixel information to restore image textures and produce visually pleasing results. Extensive experiments demonstrate that the REE can effectively help image super-resolution networks balance fidelity and perceptual quality, highlighting the great potential of Mamba in real-world applications. The source code of this work will be made publicly available at: this https URL
Abstract (translated)
多模态大型模型在通过利用语言类别作为条件信息来处理现实场景中的图像超分辨率问题时展现了出色的能力，但它们对退化图像的处理能力仍然有限。在这篇论文中，我们首先通过计算文本相似度重新审视了识别任何模型（RAM）在退化图像上的性能表现。我们发现直接使用对比学习对RAM进行微调以适应退化空间难以获得满意的结果。为了解决这个问题，我们采用了一种退化选择策略，提出了真实嵌入提取器（REE），通过对比学习，它能在退化图像内容上实现显著的识别性能提升。 此外，我们还利用了条件特征调节器（CFM）将REE的高级信息融入到强大的基于Mamba网络中，该网络能够利用有效的像素信息来恢复图像纹理，并生成视觉上令人愉悦的结果。广泛的实验表明，REE可以有效地帮助图像超分辨率网络在保真度和感知质量之间取得平衡，突显了Mamba在网络真实应用场景中的巨大潜力。这项工作的源代码将公开发布在：[提供的URL]
URL
https://arxiv.org/abs/2602.23890
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Decomposing Private Image Generation via Coarse-to-Fine Wavelet Modeling
2026-02-26 17:36:48Jasmine Bayrooti, Weiwei Kong, Natalia Ponomareva, Carlos Esteves, Ameesh Makadia, Amanda ProrokAbstract
Generative models trained on sensitive image datasets risk memorizing and reproducing individual training examples, making strong privacy guarantees essential. While differential privacy (DP) provides a principled framework for such guarantees, standard DP finetuning (e.g., with DP-SGD) often results in severe degradation of image quality, particularly in high-frequency textures, due to the indiscriminate addition of noise across all model parameters. In this work, we propose a spectral DP framework based on the hypothesis that the most privacy-sensitive portions of an image are often low-frequency components in the wavelet space (e.g., facial features and object shapes) while high-frequency components are largely generic and public. Based on this hypothesis, we propose the following two-stage framework for DP image generation with coarse image intermediaries: (1) DP finetune an autoregressive spectral image tokenizer model on the low-resolution wavelet coefficients of the sensitive images, and (2) perform high-resolution upsampling using a publicly pretrained super-resolution model. By restricting the privacy budget to the global structures of the image in the first stage, and leveraging the post-processing property of DP for detail refinement, we achieve promising trade-offs between privacy and utility. Experiments on the MS-COCO and MM-CelebA-HQ datasets show that our method generates images with improved quality and style capture relative to other leading DP image frameworks.
Abstract (translated)
训练在敏感图像数据集上的生成模型存在记忆和再现个别训练样本的风险，因此需要强有力的隐私保障。虽然差分隐私（DP）为这些保证提供了一个原则性框架，但使用标准DP微调方法（例如DP-SGD），通常会导致图像质量严重下降，特别是在高频纹理方面，这是因为在所有模型参数中无差别地添加噪声所导致的。 在本文工作中，我们提出了一种基于以下假设的光谱差分隐私框架：图像中最敏感于隐私的部分通常是小波空间中的低频成分（如面部特征和物体形状），而高频部分则大多是通用且公开的。根据这一假设，我们提出了一个两阶段框架用于具有粗略图像中间体的DP图像生成： 1. 对敏感图像的低分辨率小波系数进行差分隐私微调训练自回归光谱图像标记模型。 2. 使用公共预训练的超分辨率模型执行高分辨率上采样。 通过在第一阶段将隐私预算限制在图像的整体结构上，并利用DP的事后处理属性来细化细节，我们实现了隐私和效用之间的良好权衡。实验表明，在MS-COCO和MM-CelebA-HQ数据集上的实验结果证明了我们的方法生成的图像具有更好的质量和风格捕捉能力，相比其他领先的DP图像框架而言有显著改善。
URL
https://arxiv.org/abs/2602.23262
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CASR: A Robust Cyclic Framework for Arbitrary Large-Scale Super-Resolution with Distribution Alignment and Self-Similarity Awareness
2026-02-25 18:05:51Wenhao Guo, Zhaoran Zhao, Peng Lu, Sheng Li, Qian Qiao, RuiDe LiAbstract
Arbitrary-Scale SR (ASISR) remains fundamentally limited by cross-scale distribution shift: once the inference scale leaves the training range, noise, blur, and artifacts accumulate sharply. We revisit this challenge from a cross-scale distribution transition perspective and propose CASR, a simple yet highly efficient cyclic SR framework that reformulates ultra-magnification as a sequence of in-distribution scale transitions. This design ensures stable inference at arbitrary scales while requiring only a single model. CASR tackles two major bottlenecks: distribution drift across iterations and patch-wise diffusion inconsistencies. The proposed SDAM module aligns structural distributions via superpixel aggregation, preventing error accumulation, while SARM module restores high-frequency textures by enforcing autocorrelation and embedding LR self-similarity priors. Despite using only a single model, our approach significantly reduces distribution drift, preserves long-range texture consistency, and achieves superior generalization even at extreme magnification.
Abstract (translated)
任意尺度超分辨率（Arbitrary-Scale SR，ASISR）在本质上受到跨尺度分布偏移的限制：一旦推断尺度超出训练范围，噪声、模糊和伪影就会迅速累积。我们从跨尺度分布转换的角度重新审视这一挑战，并提出了CASR，这是一种简单但高度高效的循环SR框架，将超倍率重构重定义为一系列同分布尺度变换的过程。这种设计确保了在任意尺度上进行稳定的推断，同时只需使用一个模型即可实现。 CASR解决了两个主要瓶颈：跨迭代的分布漂移和块级扩散的一致性问题。我们提出的SDAM模块通过超级像素聚合对齐结构分布，防止错误累积；而SARM模块则通过强制自相关并嵌入低分辨率自我相似先验来恢复高频纹理。尽管仅使用一个模型，我们的方法显著减少了分布漂移，保持了长程纹理的一致性，并在极端放大时实现了优越的泛化能力。 简而言之，CASR提供了一种新颖的方法来解决ASISR中的关键问题，从而实现更加稳定、高效且具有一般性的超分辨率重构。
URL
https://arxiv.org/abs/2602.22159
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SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model
2026-02-25 11:47:00Guibin Chen, Dixuan Lin, Jiangping Yang, Youqiang Zhang, Zhengcong Fei, Debang Li, Sheng Chen, Chaofeng Ao, Nuo Pang, Yiming Wang, Yikun Dou, Zheng Chen, Mingyuan Fan, Tuanhui Li, Mingshan Chang, Hao Zhang, Xiaopeng Sun, Jingtao Xu, Yuqiang Xie, Jiahua Wang, Zhiheng Xu, Weiming Xiong, Yuzhe Jin, Baoxuan Gu, Binjie Mao, Yunjie Yu, Jujie He, Yuhao Feng, Shiwen Tu, Chaojie Wang, Rui Yan, Wei Shen, Jingchen Wu, Peng Zhao, Xuanyue Zhong, Zhuangzhuang Liu, Kaifei Wang, Fuxiang Zhang, Weikai Xu, Wenyan Liu, Binglu Zhang, Yu Shen, Tianhui Xiong, Bin Peng, Liang Zeng, Xuchen Song, Haoxiang Guo, Peiyu Wang, Yahui ZhouAbstract
SkyReels V4 is a unified multi modal video foundation model for joint video audio generation, inpainting, and editing. The model adopts a dual stream Multimodal Diffusion Transformer (MMDiT) architecture, where one branch synthesizes video and the other generates temporally aligned audio, while sharing a powerful text encoder based on the Multimodal Large Language Models (MMLM). SkyReels V4 accepts rich multi modal instructions, including text, images, video clips, masks, and audio references. By combining the MMLMs multi modal instruction following capability with in context learning in the video branch MMDiT, the model can inject fine grained visual guidance under complex conditioning, while the audio branch MMDiT simultaneously leverages audio references to guide sound generation. On the video side, we adopt a channel concatenation formulation that unifies a wide range of inpainting style tasks, such as image to video, video extension, and video editing under a single interface, and naturally extends to vision referenced inpainting and editing via multi modal prompts. SkyReels V4 supports up to 1080p resolution, 32 FPS, and 15 second duration, enabling high fidelity, multi shot, cinema level video generation with synchronized audio. To make such high resolution, long-duration generation computationally feasible, we introduce an efficiency strategy: Joint generation of low resolution full sequences and high-resolution keyframes, followed by dedicated super-resolution and frame interpolation models. To our knowledge, SkyReels V4 is the first video foundation model that simultaneously supports multi-modal input, joint video audio generation, and a unified treatment of generation, inpainting, and editing, while maintaining strong efficiency and quality at cinematic resolutions and durations.
Abstract (translated)
SkyReels V4 是一个统一的多模态视频基础模型，用于联合生成视频和音频、修复和编辑。该模型采用双流多模态扩散变换器（MMDiT）架构，其中一条分支合成视频，另一条分支生成时间对齐的音频，并共享基于多模态大型语言模型（MMLM）的强大文本编码器。SkyReels V4 接受丰富的多模态指令，包括文本、图像、视频片段、遮罩和音频参考。通过结合 MMLM 的多模态指令跟随能力和视频分支 MMDiT 中的上下文学习能力，该模型可以在复杂的条件下注入精细的视觉引导，而音频分支 MMDiT 同时利用音频参考来指导声音生成。在视频方面，我们采用通道级联的形式，统一了包括图像到视频、视频扩展和视频编辑在内的广泛修复风格任务，并通过多模态提示自然地扩展到了基于视觉参考的修复和编辑。SkyReels V4 支持高达 1080p 分辨率、32 FPS 和 15 秒时长，能够生成高保真度、多镜头、电影级别的视频并配有同步音频。为了使这种高分辨率、长时间生成在计算上可行，我们引入了一个效率策略：先联合生成低分辨率完整序列和高清关键帧，然后使用专门的超分辨率模型和帧插值模型进行后处理。据我们所知，SkyReels V4 是第一个同时支持多模态输入、联合视频音频生成，并统一处理生成、修复和编辑的视频基础模型，且在电影级分辨率和时长下保持了强大的效率和质量。
URL
https://arxiv.org/abs/2602.21818
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Physics-Consistent Diffusion for Efficient Fluid Super-Resolution via Multiscale Residual Correction
2026-02-25 03:56:34Zhihao Li, Shengwei Dong, Chuang Yi, Junxuan Gao, Zhilu Lai, Zhiqiang Liu, Wei Wang, Guangtao ZhangAbstract
Existing image SR and generic diffusion models transfer poorly to fluid SR: they are sampling-intensive, ignore physical constraints, and often yield spectral mismatch and spurious divergence. We address fluid super-resolution (SR) with \textbf{ReMD} (\underline{Re}sidual-\underline{M}ultigrid \underline{D}iffusion), a physics-consistent diffusion framework. At each reverse step, ReMD performs a \emph{multigrid residual correction}: the update direction is obtained by coupling data consistency with lightweight physics cues and then correcting the residual across scales; the multiscale hierarchy is instantiated with a \emph{multi-wavelet} basis to capture both large structures and fine vortical details. This coarse-to-fine design accelerates convergence and preserves fine structures while remaining equation-free. Across atmospheric and oceanic benchmarks, ReMD improves accuracy and spectral fidelity, reduces divergence, and reaches comparable quality with markedly fewer sampling steps than diffusion baselines. Our results show that enforcing physics consistency \emph{inside} the diffusion process via multigrid residual correction and multi-wavelet multiscale modeling is an effective route to efficient fluid SR. Our code are available on this https URL.
Abstract (translated)
现有的图像超分辨率（SR）和通用扩散模型在流体超分辨率任务中表现不佳，因为这些方法依赖于密集的采样过程、忽视物理约束，并且经常导致频谱不匹配和虚假发散。我们通过提出一种名为ReMD（Residual-Multigrid Diffusion）的基于物理学的一致性扩散框架来解决流体超分辨率问题。 在ReMD的每个逆向步骤中，采用了一种多网格残差校正方法：更新方向通过结合数据一致性与轻量级物理提示获得，并且随后跨尺度进行残差修正。这种多尺度层次结构利用了多小波（multi-wavelet）基来捕捉大规模结构和精细涡旋细节。 该自粗至细的设计不仅加速了收敛速度，而且在无需方程的情况下保留了精细的结构特征。在大气和海洋基准测试中，ReMD提高了准确性、频谱保真度，并且减少了发散现象的发生。与扩散基线模型相比，ReMD仅通过显著减少采样步骤就达到了相似的质量水平。 我们的研究结果表明，在扩散过程中通过多网格残差校正和多小波的多尺度建模强制执行物理一致性是一种实现高效流体超分辨率的有效途径。相关代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2603.00149
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Perceptual Quality Optimization of Image Super-Resolution
2026-02-25 01:17:24Wei Zhou, Yixiao Li, Hadi Amirpour, Xiaoshuai Hao, Jiang Liu, Peng Wang, Hantao LiuAbstract
Single-image super-resolution (SR) has achieved remarkable progress with deep learning, yet most approaches rely on distortion-oriented losses or heuristic perceptual priors, which often lead to a trade-off between fidelity and visual quality. To address this issue, we propose an \textit{Efficient Perceptual Bi-directional Attention Network (Efficient-PBAN)} that explicitly optimizes SR towards human-preferred quality. Unlike patch-based quality models, Efficient-PBAN avoids extensive patch sampling and enables efficient image-level perception. The proposed framework is trained on our self-constructed SR quality dataset that covers a wide range of state-of-the-art SR methods with corresponding human opinion scores. Using this dataset, Efficient-PBAN learns to predict perceptual quality in a way that correlates strongly with subjective judgments. The learned metric is further integrated into SR training as a differentiable perceptual loss, enabling closed-loop alignment between reconstruction and perceptual assessment. Extensive experiments demonstrate that our approach delivers superior perceptual quality. Code is publicly available at this https URL.
Abstract (translated)
单图像超分辨率（SR）通过深度学习取得了显著进展，但大多数方法依赖于针对失真优化的损失函数或基于直觉的人类感知先验，这往往会导致保真度和视觉质量之间的权衡。为了解决这一问题，我们提出了一种名为**Efficient Perceptual Bi-directional Attention Network (Efficient-PBAN)**的方法，该方法能够明确地优化超分辨率图像以符合人类的偏好质量标准。与基于补丁的质量模型不同，Efficient-PBAN避免了广泛的补丁采样，并且能够在图像级别实现高效感知。 我们构建了一个自有的SR质量数据集来训练所提出的框架，该数据集涵盖了多种最先进的SR方法及其对应的人类主观评分，从而使得Efficient-PBAN能够学习预测与主观评价紧密相关的感知质量。此外，我们还把学到的度量标准集成到SR模型训练中，作为可微分的感知损失函数，实现了重建和感知评估之间的闭环对齐。 通过广泛的实验验证，我们的方法在感知质量方面表现出色。代码可在以下链接公开获取：[提供的URL]（请将方括号内的文本替换为实际链接）。
URL
https://arxiv.org/abs/2602.21482
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CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models
2026-02-24 09:20:02Weining Fu, Kai Shu, Kui Xu, Qiangfeng Cliff ZhangAbstract
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling near-atomic-level visualization of biomolecular assemblies. However, the exponential growth in cryo-EM data throughput and complexity, coupled with diverse downstream analytical tasks, necessitates unified computational frameworks that transcend current task-specific deep learning approaches with limited scalability and generalizability. We present CryoLVM, a foundation model that learns rich structural representations from experimental density maps with resolved structures by leveraging the Joint-Embedding Predictive Architecture (JEPA) integrated with SCUNet-based backbone, which can be rapidly adapted to various downstream tasks. We further introduce a novel histogram-based distribution alignment loss that accelerates convergence and enhances fine-tuning performance. We demonstrate CryoLVM's effectiveness across three critical cryo-EM tasks: density map sharpening, density map super-resolution, and missing wedge restoration. Our method consistently outperforms state-of-the-art baselines across multiple density map quality metrics, confirming its potential as a versatile model for a wide spectrum of cryo-EM applications.
Abstract (translated)
低温电子显微镜（cryo-EM）通过实现生物分子组装的近原子级可视化，彻底革新了结构生物学领域。然而，随着cryo-EM数据通量和复杂性的指数增长以及各种下游分析任务的需求，迫切需要能够超越目前仅限于特定任务且可扩展性和泛化能力有限的深度学习方法的统一计算框架。我们介绍了CryoLVM，这是一种基础模型，它通过利用集成SCUNet骨干网的联合嵌入预测架构（JEPA）从具有解析结构的实验密度图中学习丰富的结构表示，可以快速适应各种下游任务。此外，我们提出了一种基于直方图的分布对齐损失函数，该函数加速了收敛速度并增强了微调性能。我们在三个关键的cryo-EM任务上展示了CryoLVM的有效性：密度图锐化、密度图超分辨率和缺失楔形恢复。我们的方法在多个密度图质量指标上始终优于最先进的基准模型，证明了其作为广泛cryo-EM应用领域灵活模型的巨大潜力。
URL
https://arxiv.org/abs/2602.02620
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GSNR: Graph Smooth Null-Space Representation for Inverse Problems
2026-02-23 20:24:00Romario Gualdr\'on-Hurtado, Roman Jacome, Rafael S. Suarez, Henry ArguelloAbstract
Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the measurements due to the non-trivial null-space of the sensing matrix. Common image priors promote solutions on the general image manifold, such as sparsity, smoothness, or score function. However, as these priors do not constrain the null-space component, they can bias the reconstruction. Thus, we aim to incorporate meaningful null-space information in the reconstruction framework. Inspired by smooth image representation on graphs, we propose Graph-Smooth Null-Space Representation (GSNR), a mechanism that imposes structure only into the invisible component. Particularly, given a graph Laplacian, we construct a null-restricted Laplacian that encodes similarity between neighboring pixels in the null-space signal, and we design a low-dimensional projection matrix from the $p$-smoothest spectral graph modes (lowest graph frequencies). This approach has strong theoretical and practical implications: i) improved convergence via a null-only graph regularizer, ii) better coverage, how much null-space variance is captured by $p$ modes, and iii) high predictability, how well these modes can be inferred from the measurements. GSNR is incorporated into well-known inverse problem solvers, e.g., PnP, DIP, and diffusion solvers, in four scenarios: image deblurring, compressed sensing, demosaicing, and image super-resolution, providing consistent improvement of up to 4.3 dB over baseline formulations and up to 1 dB compared with end-to-end learned models in terms of PSNR.
Abstract (translated)
在成像中的逆问题通常是不适定的，这导致由于感知矩阵非平凡零空间的存在，存在无数个与测量结果一致的解。常见的图像先验假设倾向于推广一般的图像流形上的解决方案，例如稀疏性、平滑度或分数函数等。然而，这些先验假设并不能约束零空间的部分，因此可能会偏置重建过程。我们的目标是将有意义的零空间信息纳入到重建框架中。 受图上平滑表示启发，我们提出了一种名为“Graph-Smooth Null-Space Representation”(GSNR) 的机制，该方法仅对不可见部分施加结构约束。具体而言，在给定一个图拉普拉斯算子的情况下，我们可以构建出一种受限的零空间拉普拉斯算子，用来编码在零空间信号中的相邻像素之间的相似性，并设计了一个从 $p$ 光滑谱图模式（最低图频率）映射到低维投影矩阵的机制。这种方法具有强大的理论和实践意义：一、通过一个仅限于零空间的图正则化器改进收敛性；二、更好的覆盖率，即多少零空间方差可以通过 $p$ 模式捕获；三、高预测能力，即这些模式从测量中可以多好地被推断出来。 GSNR 被集成到几种已知的逆问题求解器中，如PnP、DIP 和扩散求解器，在四种场景下进行了测试：图像去模糊化、压缩感知、拜耳插值和超分辨率成像。与基线方法相比，GSNR 在峰值信噪比（PSNR）方面提供了最多达 4.3 分贝的改进；在对比于端到端学习模型时，也有高达1分贝的优势。
URL
https://arxiv.org/abs/2602.20328
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InfScene-SR: Spatially Continuous Inference for Arbitrary-Size Image Super-Resolution
2026-02-23 11:34:59Shoukun Sun, Zhe Wang, Xiang Que, Jiyin Zhang, Xiaogang MaAbstract
Image Super-Resolution (SR) aims to recover high-resolution (HR) details from low-resolution (LR) inputs, a task where Denoising Diffusion Probabilistic Models (DDPMs) have recently shown superior performance compared to Generative Adversarial Networks (GANs) based approaches. However, standard diffusion-based SR models, such as SR3, are typically trained on fixed-size patches and struggle to scale to arbitrary-sized images due to memory constraints. Applying these models via independent patch processing leads to visible seams and inconsistent textures across boundaries. In this paper, we propose InfScene-SR, a framework enabling spatially continuous super-resolution for large, arbitrary scenes. We adapt the iterative refinement process of diffusion models with a novel guided and variance-corrected fusion mechanism, allowing for the seamless generation of large-scale high-resolution imagery without retraining. We validate our approach on remote sensing datasets, demonstrating that InfScene-SR not only reconstructs fine details with high perceptual quality but also eliminates boundary artifacts, benefiting downstream tasks such as semantic segmentation.
Abstract (translated)
图像超分辨率（SR）旨在从低分辨率（LR）输入中恢复高分辨率（HR）细节，这一任务最近表明，去噪扩散概率模型（DDPMs）的性能优于基于生成对抗网络（GANs）的方法。然而，标准的基于扩散的超分辨率模型（如SR3）通常是在固定大小的图像块上进行训练的，在面对任意尺寸图像时会由于内存限制而难以扩展。通过独立处理这些图像块会导致可见的接缝和跨边界不一致的纹理问题。在本文中，我们提出了InfScene-SR框架，该框架能够实现大范围、任意场景的空间连续超分辨率生成。我们通过对扩散模型的迭代细化过程进行适应，并引入了一种新的引导式且方差校正融合机制，使得无需重新训练即可无缝地生成大规模高分辨率图像。我们在遥感数据集上验证了这种方法的有效性，证明InfScene-SR不仅能够以高度感知质量重建细节，还消除了边界伪影，从而有利于下游任务如语义分割。
URL
https://arxiv.org/abs/2602.19736
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Efficient Image Super-Resolution with Multi-Scale Spatial Adaptive Attention Networks
2026-02-22 07:47:39Sushi Rao, Jingwei LiAbstract
This paper introduces a lightweight image super-resolution (SR) network, termed the Multi-scale Spatial Adaptive Attention Network (MSAAN), to address the common dilemma between high reconstruction fidelity and low model complexity in existing SR methods. The core of our approach is a novel Multi-scale Spatial Adaptive Attention Module (MSAA), designed to jointly model fine-grained local details and long-range contextual dependencies. The MSAA comprises two synergistic components: a Global Feature Modulation Module (GFM) that learns coherent texture structures through differential feature extraction, and a Multi-scale Feature Aggregation Module (MFA) that adaptively fuses features from local to global scales using pyramidal processing. To further enhance the network's capability, we propose a Local Enhancement Block (LEB) to strengthen local geometric perception and a Feature Interactive Gated Feed-Forward Module (FIGFF) to improve nonlinear representation while reducing channel redundancy. Extensive experiments on standard benchmarks (Set5, Set14, B100, Urban100, Manga109) across $\times2$, $\times3$, and $\times4$ scaling factors demonstrate that both our lightweight (MSAAN-light) and standard (MSAAN) versions achieve superior or competitive performance in terms of PSNR and SSIM, while maintaining significantly lower parameters and computational costs than state-of-the-art methods. Ablation studies validate the contribution of each component, and visual results show that MSAAN reconstructs sharper edges and more realistic textures.
Abstract (translated)
本文介绍了一种轻量级图像超分辨率（SR）网络，称为多尺度空间自适应注意力网络（MSAAN），旨在解决现有SR方法中高重建保真度与低模型复杂性之间的常见难题。我们的方法核心是一个新颖的多尺度空间自适应注意模块（MSAA），设计用于同时建模精细的局部细节和长程上下文依赖关系。MSAA包括两个协同组件：一个全局特征调制模块（GFM），通过差异特征提取来学习连贯的纹理结构，以及一个多尺度特性聚合模块（MFA），使用金字塔处理自适应地融合从局部到全球尺度的特征。 为了进一步增强网络的能力，我们提出了一种局部增强块（LEB）以强化局部几何感知，并提出了一个特性交互门控前馈模块（FIGFF）以改进非线性表示同时减少通道冗余。在标准基准测试集（Set5、Set14、B100、Urban100、Manga109）上进行了广泛的实验，涵盖$\times2$、$\times3$和$\times4$缩放因子，结果表明我们的轻量级版本（MSAAN-light）和标准版本（MSAAN）在PSNR和SSIM指标上的表现优于或与最先进的方法相当，同时保持了远低于这些方法的参数数量和计算成本。消融研究表明每个组件的重要性，并且视觉结果显示MSAAN重建出更清晰的边缘和更具现实感的纹理。
URL
https://arxiv.org/abs/2603.00118
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Robust Self-Supervised Cross-Modal Super-Resolution against Real-World Misaligned Observations
2026-02-21 12:44:36Xiaoyu Dong, Jiahuan Li, Ziteng Cui, Naoto YokoyaAbstract
We study cross-modal super-resolution (SR) on real-world misaligned data, where only a limited number of low-resolution (LR) source and high-resolution (HR) guide image pairs with complex spatial misalignments are available. To address this challenge, we propose RobSelf--a fully self-supervised model that is optimized online, requiring no training data, ground-truth supervision, or pre-alignment. RobSelf features two key techniques: a misalignment-aware feature translator and a content-aware reference filter. The translator reformulates unsupervised cross-modal and cross-resolution alignment as a weakly-supervised, misalignment-aware translation subtask, producing an aligned guide feature with inherent redundancy. Guided by this feature, the filter performs reference-based discriminative self-enhancement on the source, enabling SR predictions with high resolution and high fidelity. Across a variety of tasks, we demonstrate that RobSelf achieves state-of-the-art performance and superior efficiency. Additionally, we introduce a real-world dataset, RealMisSR, to advance research on this topic. Dataset and code: this https URL.
Abstract (translated)
我们研究了在现实世界中不匹配数据上的跨模态超分辨率（SR）问题，其中仅提供了少量低分辨率（LR）源图像与高分辨率（HR）引导图像的配对组合，并且这些图像存在复杂的空间错位。为了解决这一挑战，我们提出了一种名为RobSelf的完全自监督模型，该模型在线优化，无需训练数据、真实标签监督或预对齐步骤。 RobSelf具备两个关键技术： 1. 错位感知特征翻译器：将无监督跨模态和跨分辨率对齐重新定义为一个弱监督、错位感知的翻译子任务。此过程会产生一种包含冗余信息且与源图像对齐的引导特征。 2. 内容感知参考过滤器：基于上述生成的特征，该过滤器执行以参考为基础的判别性自我增强操作，从而在源图像上实现高分辨率和高质量的超分辨率预测。 我们在多种任务中展示了RobSelf取得了最先进的性能，并且具有卓越的效率。此外，我们还引入了一个真实世界的实验数据集RealMisSR，用以推进该领域的研究进展。有关数据集和代码，请参见此链接：this https URL.
URL
https://arxiv.org/abs/2602.18822
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Physics Encoded Spatial and Temporal Generative Adversarial Network for Tropical Cyclone Image Super-resolution
2026-02-19 11:26:01Ruoyi Zhang, Jiawei Yuan, Lujia Ye, Runling Yu, Liling ZhaoAbstract
High-resolution satellite imagery is indispensable for tracking the genesis, intensification, and trajectory of tropical cyclones (TCs). However, existing deep learning-based super-resolution (SR) methods often treat satellite image sequences as generic videos, neglecting the underlying atmospheric physical laws governing cloud motion. To address this, we propose a Physics Encoded Spatial and Temporal Generative Adversarial Network (PESTGAN) for TC image super-resolution. Specifically, we design a disentangled generator architecture incorporating a PhyCell module, which approximates the vorticity equation via constrained convolutions and encodes the resulting approximate physical dynamics as implicit latent representations to separate physical dynamics from visual textures. Furthermore, a dual-discriminator framework is introduced, employing a temporal discriminator to enforce motion consistency alongside spatial realism. Experiments on the Digital Typhoon dataset for 4$\times$ upscaling demonstrate that PESTGAN establishes a better performance in structural fidelity and perceptual quality. While maintaining competitive pixel-wise accuracy compared to existing approaches, our method significantly excels in reconstructing meteorologically plausible cloud structures with superior physical fidelity.
Abstract (translated)
URL
https://arxiv.org/abs/2602.17277
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RPT-SR: Regional Prior attention Transformer for infrared image Super-Resolution
2026-02-17 10:56:49Youngwan Jin, Incheol Park, Yagiz Nalcakan, Hyeongjin Ju, Sanghyeop Yeo, Shiho KimAbstract
General-purpose super-resolution models, particularly Vision Transformers, have achieved remarkable success but exhibit fundamental inefficiencies in common infrared imaging scenarios like surveillance and autonomous driving, which operate from fixed or nearly-static viewpoints. These models fail to exploit the strong, persistent spatial priors inherent in such scenes, leading to redundant learning and suboptimal performance. To address this, we propose the Regional Prior attention Transformer for infrared image Super-Resolution (RPT-SR), a novel architecture that explicitly encodes scene layout information into the attention mechanism. Our core contribution is a dual-token framework that fuses (1) learnable, regional prior tokens, which act as a persistent memory for the scene's global structure, with (2) local tokens that capture the frame-specific content of the current input. By utilizing these tokens into an attention, our model allows the priors to dynamically modulate the local reconstruction process. Extensive experiments validate our approach. While most prior works focus on a single infrared band, we demonstrate the broad applicability and versatility of RPT-SR by establishing new state-of-the-art performance across diverse datasets covering both Long-Wave (LWIR) and Short-Wave (SWIR) spectra
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URL
https://arxiv.org/abs/2602.15490
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Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shift
2026-02-16 20:11:42Xiaoyi Wen, Fei JiangAbstract
Super-resolution is widely used in medical imaging to enhance low-quality data, reducing scan time and improving abnormality detection. Conventional super-resolution approaches typically rely on paired datasets of downsampled and original high resolution images, training models to reconstruct high resolution images from their artificially degraded counterparts. However, in real-world clinical settings, low resolution data often arise from acquisition mechanisms that differ significantly from simple downsampling. As a result, these inputs may lie outside the domain of the training data, leading to poor model generalization due to domain shift. To address this limitation, we propose a distributional deep learning framework that improves model robustness and domain generalization. We develop this approch for enhancing the resolution of 4D Flow MRI (4DF). This is a novel imaging modality that captures hemodynamic flow velocity and clinically relevant metrics such as vessel wall stress. These metrics are critical for assessing aneurysm rupture risk. Our model is initially trained on high resolution computational fluid dynamics (CFD) simulations and their downsampled counterparts. It is then fine-tuned on a small, harmonized dataset of paired 4D Flow MRI and CFD samples. We derive the theoretical properties of our distributional estimators and demonstrate that our framework significantly outperforms traditional deep learning approaches through real data applications. This highlights the effectiveness of distributional learning in addressing domain shift and improving super-resolution performance in clinically realistic scenarios.
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URL
https://arxiv.org/abs/2602.15167
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A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving
2026-02-15 22:34:28June Moh Goo, Zichao Zeng, Jan BoehmAbstract
LiDAR sensors are often considered essential for autonomous driving, but high-resolution sensors remain expensive while affordable low-resolution sensors produce sparse point clouds that miss critical details. LiDAR super-resolution addresses this challenge by using deep learning to enhance sparse point clouds, bridging the gap between different sensor types and enabling cross-sensor compatibility in real-world deployments. This paper presents the first comprehensive survey of LiDAR super-resolution methods for autonomous driving. Despite the importance of practical deployment, no systematic review has been conducted until now. We organize existing approaches into four categories: CNN-based architectures, model-based deep unrolling, implicit representation methods, and Transformer and Mamba-based approaches. We establish fundamental concepts including data representations, problem formulation, benchmark datasets and evaluation metrics. Current trends include the adoption of range image representation for efficient processing, extreme model compression and the development of resolution-flexible architectures. Recent research prioritizes real-time inference and cross-sensor generalization for practical deployment. We conclude by identifying open challenges and future research directions for advancing LiDAR super-resolution technology.
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URL
https://arxiv.org/abs/2602.15904
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FUTON: Fourier Tensor Network for Implicit Neural Representations
2026-02-13 19:31:44Pooya Ashtari, Pourya Behmandpoor, Nikos Deligiannis, Aleksandra PizuricaAbstract
Implicit neural representations (INRs) have emerged as powerful tools for encoding signals, yet dominant MLP-based designs often suffer from slow convergence, overfitting to noise, and poor extrapolation. We introduce FUTON (Fourier Tensor Network), which models signals as generalized Fourier series whose coefficients are parameterized by a low-rank tensor decomposition. FUTON implicitly expresses signals as weighted combinations of orthonormal, separable basis functions, combining complementary inductive biases: Fourier bases capture smoothness and periodicity, while the low-rank parameterization enforces low-dimensional spectral structure. We provide theoretical guarantees through a universal approximation theorem and derive an inference algorithm with complexity linear in the spectral resolution and the input dimension. On image and volume representation, FUTON consistently outperforms state-of-the-art MLP-based INRs while training 2--5$\times$ faster. On inverse problems such as image denoising and super-resolution, FUTON generalizes better and converges faster.
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URL
https://arxiv.org/abs/2602.13414
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