Super_Resolution
-
Super-r'esolution non supervis'ee d'images hyperspectrales de t'el'ed'etection utilisant un entra^inement enti`erement synth'etique
2026-01-30 10:32:56Xinxin Xu, Yann Gousseau, Christophe Kervazo, Sa\"id LadjalAbstract
Hyperspectral single image super-resolution (SISR) aims to enhance spatial resolution while preserving the rich spectral information of hyperspectral images. Most existing methods rely on supervised learning with high-resolution ground truth data, which is often unavailable in practice. To overcome this limitation, we propose an unsupervised learning approach based on synthetic abundance data. The hyperspectral image is first decomposed into endmembers and abundance maps through hyperspectral unmixing. A neural network is then trained to super-resolve these maps using data generated with the dead leaves model, which replicates the statistical properties of real abundances. The final super-resolution hyperspectral image is reconstructed by recombining the super-resolved abundance maps with the endmembers. Experimental results demonstrate the effectiveness of our method and the relevance of synthetic data for training.
Abstract (translated)
高光谱单幅图像超分辨率（SISR）旨在增强空间分辨率，同时保持高光谱图像的丰富光谱信息。大多数现有的方法依赖于具有高质量地面真实数据的监督学习，而这些数据在实际应用中往往不可用。为了解决这一限制，我们提出了一种基于合成丰度数据的无监督学习方法。首先通过高光谱解混将高光谱图像分解成端元和丰度图。然后训练神经网络使用死叶片模型生成的数据来对这些地图进行超分辨率处理，该模型复制了真实丰度的统计特性。最后，通过重新组合超分辨率丰度图与端元，重建出最终的超分辨高光谱图像。实验结果证明了我们方法的有效性以及合成数据在训练中的相关性和实用性。
URL
https://arxiv.org/abs/2602.02552
PDF
-
Stabilizing Diffusion Posterior Sampling by Noise--Frequency Continuation
2026-01-30 03:14:01Feng Tian, Yixuan Li, Weili Zeng, Weitian Zhang, Yichao Yan, Xiaokang YangAbstract
Diffusion posterior sampling solves inverse problems by combining a pretrained diffusion prior with measurement-consistency guidance, but it often fails to recover fine details because measurement terms are applied in a manner that is weakly coupled to the diffusion noise level. At high noise, data-consistency gradients computed from inaccurate estimates can be geometrically incongruent with the posterior geometry, inducing early-step drift, spurious high-frequency artifacts, plus sensitivity to schedules and ill-conditioned operators. To address these concerns, we propose a noise--frequency Continuation framework that constructs a continuous family of intermediate posteriors whose likelihood enforces measurement consistency only within a noise-dependent frequency band. This principle is instantiated with a stabilized posterior sampler that combines a diffusion predictor, band-limited likelihood guidance, and a multi-resolution consistency strategy that aggressively commits reliable coarse corrections while conservatively adopting high-frequency details only when they become identifiable. Across super-resolution, inpainting, and deblurring, our method achieves state-of-the-art performance and improves motion deblurring PSNR by up to 5 dB over strong baselines.
Abstract (translated)
URL
https://arxiv.org/abs/2602.00176
PDF
-
MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
2026-01-29 17:52:41Baorui Ma, Jiahui Yang, Donglin Di, Xuancheng Zhang, Jianxun Cui, Hao Li, Yan Xie, Wei ChenAbstract
Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data. We introduce Metric Anything, a simple and scalable pretraining framework that learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera-specific modeling, or task-specific architectures. Central to our approach is the Sparse Metric Prompt, created by randomly masking depth maps, which serves as a universal interface that decouples spatial reasoning from sensor and camera biases. Using about 20M image-depth pairs spanning reconstructed, captured, and rendered 3D data across 10000 camera models, we demonstrate-for the first time-a clear scaling trend in the metric depth track. The pretrained model excels at prompt-driven tasks such as depth completion, super-resolution and Radar-camera fusion, while its distilled prompt-free student achieves state-of-the-art results on monocular depth estimation, camera intrinsics recovery, single/multi-view metric 3D reconstruction, and VLA planning. We also show that using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence. These results show that metric depth estimation can benefit from the same scaling laws that drive modern foundation models, establishing a new path toward scalable and efficient real-world metric perception. We open-source MetricAnything at this http URL to support community research.
Abstract (translated)
URL
https://arxiv.org/abs/2601.22054
PDF
-
SR$^{2}$-Net: A General Plug-and-Play Model for Spectral Refinement in Hyperspectral Image Super-Resolution
2026-01-29 07:00:00Ji-Xuan He, Guohang Zhuang, Junge Bo, Tingyi Li, Chen Ling, Yanan QiaoAbstract
HSI-SR aims to enhance spatial resolution while preserving spectrally faithful and physically plausible characteristics. Recent methods have achieved great progress by leveraging spatial correlations to enhance spatial resolution. However, these methods often neglect spectral consistency across bands, leading to spurious oscillations and physically implausible artifacts. While spectral consistency can be addressed by designing the network architecture, it results in a loss of generality and flexibility. To address this issue, we propose a lightweight plug-and-play rectifier, physically priors Spectral Rectification Super-Resolution Network (SR$^{2}$-Net), which can be attached to a wide range of HSI-SR models without modifying their architectures. SR$^{2}$-Net follows an enhance-then-rectify pipeline consisting of (i) Hierarchical Spectral-Spatial Synergy Attention (H-S$^{3}$A) to reinforce cross-band interactions and (ii) Manifold Consistency Rectification (MCR) to constrain the reconstructed spectra to a compact, physically plausible spectral manifold. In addition, we introduce a degradation-consistency loss to enforce data fidelity by encouraging the degraded SR output to match the observed low resolution input. Extensive experiments on multiple benchmarks and diverse backbones demonstrate consistent improvements in spectral fidelity and overall reconstruction quality with negligible computational overhead. Our code will be released upon publication.
Abstract (translated)
URL
https://arxiv.org/abs/2601.21338
PDF
-
OSDEnhancer: Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion
2026-01-28 06:59:55Shuoyan Wei, Feng Li, Chen Zhou, Runmin Cong, Yao Zhao, Huihui BaiAbstract
Diffusion models (DMs) have demonstrated exceptional success in video super-resolution (VSR), showcasing a powerful capacity for generating fine-grained details. However, their potential for space-time video super-resolution (STVSR), which necessitates not only recovering realistic visual content from low-resolution to high-resolution but also improving the frame rate with coherent temporal dynamics, remains largely underexplored. Moreover, existing STVSR methods predominantly address spatiotemporal upsampling under simplified degradation assumptions, which often struggle in real-world scenarios with complex unknown degradations. Such a high demand for reconstruction fidelity and temporal consistency makes the development of a robust STVSR framework particularly non-trivial. To address these challenges, we propose OSDEnhancer, a novel framework that, to the best of our knowledge, represents the first method to achieve real-world STVSR through an efficient one-step diffusion process. OSDEnhancer initializes essential spatiotemporal structures through a linear pre-interpolation strategy and pivots on training temporal refinement and spatial enhancement mixture of experts (TR-SE MoE), which allows distinct expert pathways to progressively learn robust, specialized representations for temporal coherence and spatial detail, further collaboratively reinforcing each other during inference. A bidirectional deformable variational autoencoder (VAE) decoder is further introduced to perform recurrent spatiotemporal aggregation and propagation, enhancing cross-frame reconstruction fidelity. Experiments demonstrate that the proposed method achieves state-of-the-art performance while maintaining superior generalization capability in real-world scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2601.20308
PDF
-
A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS
2026-01-27 14:01:29Lucas de Magalh\~aes Araujo, Ot\'avio Oliveira Napoli, Sandra Avila, Edson BorinAbstract
We introduce the Unicamp-NAMSS dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics. We constructed the dataset from the National Archive of Marine Seismic Surveys (NAMSS), which contains decades of publicly available marine seismic data acquired across multiple regions, acquisition conditions, and geological settings. After a comprehensive collection and filtering process, we obtained 2588 cleaned and standardized seismic sections from 122 survey areas, covering a wide range of vertical and horizontal sampling characteristics. To ensure reliable experimentation, we balanced the dataset so that no survey dominates the distribution, and partitioned it into non-overlapping macro-regions for training, validation, and testing. This region-disjoint split allows robust evaluation of generalization to unseen geological and acquisition conditions. We validated the dataset through quantitative and embedding-space analyses using both convolutional and transformer-based models. These analyses showed that Unicamp-NAMSS exhibits substantial variability within and across regions, while maintaining coherent structure across acquisition macro-region and survey types. Comparisons with widely used interpretation datasets (Parihaka and F3 Block) further demonstrated that Unicamp-NAMSS covers a broader portion of the seismic appearance space, making it a strong candidate for machine learning model pretraining. The dataset, therefore, provides a valuable resource for machine learning tasks, including self-supervised representation learning, transfer learning, benchmarking supervised tasks such as super-resolution or attribute prediction, and studying domain adaptation in seismic interpretation.
Abstract (translated)
我们介绍了Unicamp-NAMSS数据集，这是一个大型、多样且地理分布广泛的数据集合，包含迁移的二维地震剖面，旨在支持地球物理学领域的现代机器学习研究。该数据集由国家海洋地震调查档案（NAMSS）构建而成，NAMSS包含了数十年来在多个地区和地质条件下获得并公开的海上地震数据。 经过全面收集和过滤过程后，我们从122个调查区域中获得了2588份清洗和标准化后的地震剖面，涵盖了广泛的垂直和水平采样特性。为了确保可靠的实验结果，我们将该数据集进行了平衡处理，以保证任何单一调查都不会占据主导地位，并将其划分成互不重叠的宏区用于训练、验证和测试。这种区域分离的分割方式允许对模型在未见过地质和获取条件下的泛化能力进行稳健评估。 我们使用卷积和基于Transformer的模型通过定量分析及嵌入空间分析对该数据集进行了验证，结果显示Unicamp-NAMSS在不同地区之间以及各个内部都表现出显著的变化性，并且在整个采集宏区和调查类型上保持了连贯的结构。与广泛使用的解释数据集（Parihaka和F3区块）进行对比进一步表明，Unicamp-NAMSS覆盖了更广泛的地震外观空间部分，使其成为机器学习模型预训练的一个强有力候选者。 因此，该数据集为包括自我监督表征学习、迁移学习、基准测试如超分辨率或属性预测任务以及研究地震解释中的领域适应性在内的各种机器学习任务提供了一个宝贵的资源。
URL
https://arxiv.org/abs/2602.04890
PDF
-
GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution
2026-01-27 03:43:39Yongsong Huang, Tzu-Hsuan Peng, Tomo Miyazaki, Xiaofeng Liu, Chun-Ting Chou, Ai-Chun Pang, Shinichiro OmachiAbstract
Low-light image super-resolution (LLSR) is a challenging task due to the coupled degradation of low resolution and poor illumination. To address this, we propose the Guided Texture and Feature Modulation Network (GTFMN), a novel framework that decouples the LLSR task into two sub-problems: illumination estimation and texture restoration. First, our network employs a dedicated Illumination Stream whose purpose is to predict a spatially varying illumination map that accurately captures lighting distribution. Further, this map is utilized as an explicit guide within our novel Illumination Guided Modulation Block (IGM Block) to dynamically modulate features in the Texture Stream. This mechanism achieves spatially adaptive restoration, enabling the network to intensify enhancement in poorly lit regions while preserving details in well-exposed areas. Extensive experiments demonstrate that GTFMN achieves the best performance among competing methods on the OmniNormal5 and OmniNormal15 datasets, outperforming them in both quantitative metrics and visual quality.
Abstract (translated)
URL
https://arxiv.org/abs/2601.19157
PDF
-
Implicit Neural Representation-Based Continuous Single Image Super Resolution: An Empirical Study
2026-01-25 07:09:20Tayyab Nasir, Daochang Liu, Ajmal MianAbstract
Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR). To date, no empirical study has systematically examined the effectiveness of existing methods, nor investigated the effects of different training recipes, such as scaling laws, objective design, and optimization strategies. A rigorous empirical analysis is essential not only for benchmarking performance and revealing true gains but also for establishing the current state of ASSR, identifying saturation limits, and highlighting promising directions. We fill this gap by comparing existing techniques across diverse settings and presenting aggregated performance results on multiple image quality metrics. We contribute a unified framework and code repository to facilitate reproducible comparisons. Furthermore, we investigate the impact of carefully controlled training configurations on perceptual image quality and examine a new loss function that penalizes intensity variations while preserving edges, textures, and finer details during training. We conclude the following key insights that have been previously overlooked: (1) Recent, more complex INR methods provide only marginal improvements over earlier methods. (2) Model performance is strongly correlated to training configurations, a factor overlooked in prior works. (3) The proposed loss enhances texture fidelity across architectures, emphasizing the role of objective design for targeted perceptual gains. (4) Scaling laws apply to INR-based ASSR, confirming predictable gains with increased model complexity and data diversity.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17723
PDF
-
Coronary Artery Segmentation and Vessel-Type Classification in X-Ray Angiography
2026-01-24 11:56:49Mehdi Yousefzadeh, Siavash Shirzadeh Barough, Ashkan Fakharifar, Yashar Tayyarazad, Narges Eghbali, Mohaddeseh Mozaffari, Hoda Taeb, Negar Sadat Rafiee Tabatabaee, Parsa Esfahanian, Ghazaleh Sadeghi Gohar, Amineh Safavirad, Saeideh Mazloomzadeh, Ehsan khalilipur, Armin Elahifar, Majid MalekiAbstract
X-ray coronary angiography (XCA) is the clinical reference standard for assessing coronary artery disease, yet quantitative analysis is limited by the difficulty of robust vessel segmentation in routine data. Low contrast, motion, foreshortening, overlap, and catheter confounding degrade segmentation and contribute to domain shift across centers. Reliable segmentation, together with vessel-type labeling, enables vessel-specific coronary analytics and downstream measurements that depend on anatomical localization. From 670 cine sequences (407 subjects), we select a best frame near peak opacification using a low-intensity histogram criterion and apply joint super-resolution and enhancement. We benchmark classical Meijering, Frangi, and Sato vesselness filters under per-image oracle tuning, a single global mean setting, and per-image parameter prediction via Support Vector Regression (SVR). Neural baselines include U-Net, FPN, and a Swin Transformer, trained with coronary-only and merged coronary+catheter supervision. A second stage assigns vessel identity (LAD, LCX, RCA). External evaluation uses the public DCA1 cohort. SVR per-image tuning improves Dice over global means for all classical filters (e.g., Frangi: 0.759 vs. 0.741). Among deep models, FPN attains 0.914+/-0.007 Dice (coronary-only), and merged coronary+catheter labels further improve to 0.931+/-0.006. On DCA1 as a strict external test, Dice drops to 0.798 (coronary-only) and 0.814 (merged), while light in-domain fine-tuning recovers to 0.881+/-0.014 and 0.882+/-0.015. Vessel-type labeling achieves 98.5% accuracy (Dice 0.844) for RCA, 95.4% (0.786) for LAD, and 96.2% (0.794) for LCX. Learned per-image tuning strengthens classical pipelines, while high-resolution FPN models and merged-label supervision improve stability and external transfer with modest adaptation.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17429
PDF
-
TEXTS-Diff: TEXTS-Aware Diffusion Model for Real-World Text Image Super-Resolution
2026-01-24 07:03:41Haodong He, Xin Zhan, Yancheng Bai, Rui Lan, Lei Sun, Xiangxiang ChuAbstract
Real-world text image super-resolution aims to restore overall visual quality and text legibility in images suffering from diverse degradations and text distortions. However, the scarcity of text image data in existing datasets results in poor performance on text regions. In addition, datasets consisting of isolated text samples limit the quality of background reconstruction. To address these limitations, we construct Real-Texts, a large-scale, high-quality dataset collected from real-world images, which covers diverse scenarios and contains natural text instances in both Chinese and English. Additionally, we propose the TEXTS-Aware Diffusion Model (TEXTS-Diff) to achieve high-quality generation in both background and textual regions. This approach leverages abstract concepts to improve the understanding of textual elements within visual scenes and concrete text regions to enhance textual details. It mitigates distortions and hallucination artifacts commonly observed in text regions, while preserving high-quality visual scene fidelity. Extensive experiments demonstrate that our method achieves state-of-the-art performance across multiple evaluation metrics, exhibiting superior generalization ability and text restoration accuracy in complex scenarios. All the code, model, and dataset will be released.
Abstract (translated)
URL
https://arxiv.org/abs/2601.17340
PDF
-
Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
2026-01-23 11:25:04Maxence Noble, Gonzalo I\~naki Quintana, Benjamin Aubin, Cl\'ement ChadebecAbstract
Diffusion-based image super-resolution (SR) has recently attracted significant attention by leveraging the expressive power of large pre-trained text-to-image diffusion models (DMs). A central practical challenge is resolving the trade-off between reconstruction faithfulness and photorealism. To address inference efficiency, many recent works have explored knowledge distillation strategies specifically tailored to SR, enabling one-step diffusion-based approaches. However, these teacher-student formulations are inherently constrained by information compression, which can degrade perceptual cues such as lifelike textures and depth of field, even with high overall perceptual quality. In parallel, self-distillation DMs, known as Flow Map models, have emerged as a promising alternative for image generation tasks, enabling fast inference while preserving the expressivity and training stability of standard DMs. Building on these developments, we propose FlowMapSR, a novel diffusion-based framework for image super-resolution explicitly designed for efficient inference. Beyond adapting Flow Map models to SR, we introduce two complementary enhancements: (i) positive-negative prompting guidance, based on a generalization of classifier free-guidance paradigm to Flow Map models, and (ii) adversarial fine-tuning using Low-Rank Adaptation (LoRA). Among the considered Flow Map formulations (Eulerian, Lagrangian, and Shortcut), we find that the Shortcut variant consistently achieves the best performance when combined with these enhancements. Extensive experiments show that FlowMapSR achieves a better balance between reconstruction faithfulness and photorealism than recent state-of-the-art methods for both x4 and x8 upscaling, while maintaining competitive inference time. Notably, a single model is used for both upscaling factors, without any scale-specific conditioning or degradation-guided mechanisms.
Abstract (translated)
URL
https://arxiv.org/abs/2601.16660
PDF
-
DeMark: A Query-Free Black-Box Attack on Deepfake Watermarking Defenses
2026-01-23 06:04:43Wei Song, Zhenchang Xing, Liming Zhu, Yulei Sui, Jingling XueAbstract
The rapid proliferation of realistic deepfakes has raised urgent concerns over their misuse, motivating the use of defensive watermarks in synthetic images for reliable detection and provenance tracking. However, this defense paradigm assumes such watermarks are inherently resistant to removal. We challenge this assumption with DeMark, a query-free black-box attack framework that targets defensive image watermarking schemes for deepfakes. DeMark exploits latent-space vulnerabilities in encoder-decoder watermarking models through a compressive sensing based sparsification process, suppressing watermark signals while preserving perceptual and structural realism appropriate for deepfakes. Across eight state-of-the-art watermarking schemes, DeMark reduces watermark detection accuracy from 100% to 32.9% on average while maintaining natural visual quality, outperforming existing attacks. We further evaluate three defense strategies, including image super resolution, sparse watermarking, and adversarial training, and find them largely ineffective. These results demonstrate that current encoder decoder watermarking schemes remain vulnerable to latent-space manipulations, underscoring the need for more robust watermarking methods to safeguard against deepfakes.
Abstract (translated)
URL
https://arxiv.org/abs/2601.16473
PDF
-
A Cosine Network for Image Super-Resolution
2026-01-23 02:58:57Chunwei Tian, Chengyuan Zhang, Bob Zhang, Zhiwu Li, C. L. Philip Chen, David ZhangAbstract
Deep convolutional neural networks can use hierarchical information to progressively extract structural information to recover high-quality images. However, preserving the effectiveness of the obtained structural information is important in image super-resolution. In this paper, we propose a cosine network for image super-resolution (CSRNet) by improving a network architecture and optimizing the training strategy. To extract complementary homologous structural information, odd and even heterogeneous blocks are designed to enlarge the architectural differences and improve the performance of image super-resolution. Combining linear and non-linear structural information can overcome the drawback of homologous information and enhance the robustness of the obtained structural information in image super-resolution. Taking into account the local minimum of gradient descent, a cosine annealing mechanism is used to optimize the training procedure by performing warm restarts and adjusting the learning rate. Experimental results illustrate that the proposed CSRNet is competitive with state-of-the-art methods in image super-resolution.
Abstract (translated)
URL
https://arxiv.org/abs/2601.16413
PDF
-
Enhancing Text-to-Image Generation via End-Edge Collaborative Hybrid Super-Resolution
2026-01-21 07:55:37Chongbin Yi, Yuxin Liang, Ziqi Zhou, Peng YangAbstract
Artificial Intelligence-Generated Content (AIGC) has made significant strides, with high-resolution text-to-image (T2I) generation becoming increasingly critical for improving users' Quality of Experience (QoE). Although resource-constrained edge computing adequately supports fast low-resolution T2I generations, achieving high-resolution output still faces the challenge of ensuring image fidelity at the cost of latency. To address this, we first investigate the performance of super-resolution (SR) methods for image enhancement, confirming a fundamental trade-off that lightweight learning-based SR struggles to recover fine details, while diffusion-based SR achieves higher fidelity at a substantial computational cost. Motivated by these observations, we propose an end-edge collaborative generation-enhancement framework. Upon receiving a T2I generation task, the system first generates a low-resolution image based on adaptively selected denoising steps and super-resolution scales at the edge side, which is then partitioned into patches and processed by a region-aware hybrid SR policy. This policy applies a diffusion-based SR model to foreground patches for detail recovery and a lightweight learning-based SR model to background patches for efficient upscaling, ultimately stitching the enhanced ones into the high-resolution image. Experiments show that our system reduces service latency by 33% compared with baselines while maintaining competitive image quality.
Abstract (translated)
人工智能生成内容（AIGC）取得了显著进展，其中高分辨率的文本到图像（T2I）生成变得越来越重要，以提升用户的体验质量（QoE）。尽管资源受限的边缘计算能够支持快速生成低分辨率的T2I输出，但要实现高质量的高分辨率图像仍面临延迟和图像保真度之间的权衡。为了解决这一问题，我们首先研究了超分辨率（SR）方法在图像增强方面的性能，发现了一个基本的折中：轻量级学习型SR难以恢复精细细节，而基于扩散的SR虽然能够达到更高的图像保真度，但计算成本较高。 鉴于这些观察结果，我们提出了一种端边协作生成与增强框架。当系统收到一个T2I生成任务时，在边缘侧根据自适应选择的去噪步骤和超分辨率尺度先生成一张低分辨率图片。接着将该图片分割成多个补丁，并采用一种区域感知混合SR策略进行处理：此策略使用基于扩散的SR模型对前景补丁进行细节恢复，同时利用轻量级学习型SR模型快速扩大背景补丁尺寸，最终将其拼接为高分辨率图像。 实验表明，与基准系统相比，我们的框架在保持竞争性图像质量的同时，能够将服务延迟降低33%。
URL
https://arxiv.org/abs/2601.14741
PDF
-
Quantum Super-resolution by Adaptive Non-local Observables
2026-01-20 19:40:59Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Shinjae YooAbstract
Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but require increasingly deeper networks, large datasets, and heavy computation to capture fine-grained correlations. In this work, we present the \emph{first study} to investigate quantum circuits for SR. We propose a framework based on Variational Quantum Circuits (VQCs) with \emph{Adaptive Non-Local Observable} (ANO) measurements. Unlike conventional VQCs with fixed Pauli readouts, ANO introduces trainable multi-qubit Hermitian observables, allowing the measurement process to adapt during training. This design leverages the high-dimensional Hilbert space of quantum systems and the representational structure provided by entanglement and superposition. Experiments demonstrate that ANO-VQCs achieve up to five-fold higher resolution with a relatively small model size, suggesting a promising new direction at the intersection of quantum machine learning and super-resolution.
Abstract (translated)
超分辨率（SR）的目标是从低分辨率（LR）的观测数据中重建高分辨率（HR）的数据。传统的深度学习方法已经大大推进了超分辨率技术的发展，但这些方法需要越来越深的网络、大型数据集以及大量的计算资源来捕捉细微的相关性。在这项工作中，我们首次研究了量子电路在超分辨率中的应用。我们提出了一个基于变分量子线路（VQCs）并采用自适应非局部可观测量（ANO）的框架。 与传统的具有固定泡利读出操作的VQC不同，ANO引入了可训练的多量子比特厄米算子，使得测量过程可以在训练过程中进行调整和优化。这种设计利用了量子系统的高维希尔伯特空间以及纠缠和叠加提供的表示结构。实验结果表明，ANO-VQCs在模型规模相对较小的情况下可以实现高达五倍分辨率的提升，这表明在量子机器学习与超分辨率技术交叉领域中存在一个有前景的新发展方向。
URL
https://arxiv.org/abs/2601.14433
PDF
-
Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution
2026-01-20 14:53:20Samuel W. Remedios, Zhangxing Bian, Shuwen Wei, Aaron Carass, Jerry L. Prince, Blake E. DeweyAbstract
Diffusion models are the current state-of-the-art for solving inverse problems in imaging. Their impressive generative capability allows them to approximate sampling from a prior distribution, which alongside a known likelihood function permits posterior sampling without retraining the model. While recent methods have made strides in advancing the accuracy of posterior sampling, the majority focuses on single-image inverse problems. However, for modalities such as magnetic resonance imaging (MRI), it is common to acquire multiple complementary measurements, each low-resolution along a different axis. In this work, we generalize common diffusion-based inverse single-image problem solvers for multi-image super-resolution (MISR) MRI. We show that the DPS likelihood correction allows an exactly-separable gradient decomposition across independently acquired measurements, enabling MISR without constructing a joint operator, modifying the diffusion model, or increasing network function evaluations. We derive MISR versions of DPS, DMAP, DPPS, and diffusion-based PnP/ADMM, and demonstrate substantial gains over SISR across $4\times/8\times/16\times$ anisotropic degradations. Our results achieve state-of-the-art super-resolution of anisotropic MRI volumes and, critically, enable reconstruction of near-isotropic anatomy from routine 2D multi-slice acquisitions, which are otherwise highly degraded in orthogonal views.
Abstract (translated)
扩散模型目前是解决成像逆问题的最先进方法。它们令人印象深刻的生成能力允许从先验分布中近似采样，结合已知的概率函数可以无需重新训练模型就能进行后验采样。尽管最近的方法在提高后验采样的准确性方面取得了进展，但大多数研究都集中在单一图像的逆向问题上。然而，在诸如磁共振成像（MRI）等模式下，通常会获取多个互补测量值，每个测量值沿不同轴低分辨率。 在这项工作中，我们将常见的基于扩散模型解决单张图像逆向问题的方法推广到了多图超分辨率（MISR）MRI。我们展示了DPS概率修正使得在独立获取的测量之间可以精确分离梯度分解，从而能够实现无需构建联合算子、修改扩散模型或增加网络函数评估次数的情况下进行MISR。 我们推导了适用于MISR的DPS、DMAP、DPPS和基于扩散的PnP/ADMM方法，并展示了它们在$4\times/8\times/16\times$各向异性退化情况下的显著性能提升。我们的结果实现了各向异性MRI体素的超分辨率的新纪录，更重要的是，这些结果能够从常规2D多切片获取中重建近似等方向解剖结构，在正交视图下这些获取通常会严重退化。 总的来说，这项研究展示了如何通过利用扩散模型和精确的概率修正技术来解决复杂的多图像逆问题，并且在MRI超分辨率领域取得了重要的进展。
URL
https://arxiv.org/abs/2601.14030
PDF
-
SHARE: A Fully Unsupervised Framework for Single Hyperspectral Image Restoration
2026-01-20 14:01:13Jiangwei Xie, Zhang Wen, Mike Davies, Dongdong ChenAbstract
Hyperspectral image (HSI) restoration is a fundamental challenge in computational imaging and computer vision. It involves ill-posed inverse problems, such as inpainting and super-resolution. Although deep learning methods have transformed the field through data-driven learning, their effectiveness hinges on access to meticulously curated ground-truth datasets. This fundamentally restricts their applicability in real-world scenarios where such data is unavailable. This paper presents SHARE (Single Hyperspectral Image Restoration with Equivariance), a fully unsupervised framework that unifies geometric equivariance principles with low-rank spectral modelling to eliminate the need for ground truth. SHARE's core concept is to exploit the intrinsic invariance of hyperspectral structures under differentiable geometric transformations (e.g. rotations and scaling) to derive self-supervision signals through equivariance consistency constraints. Our novel Dynamic Adaptive Spectral Attention (DASA) module further enhances this paradigm shift by explicitly encoding the global low-rank property of HSI and adaptively refining local spectral-spatial correlations through learnable attention mechanisms. Extensive experiments on HSI inpainting and super-resolution tasks demonstrate the effectiveness of SHARE. Our method outperforms many state-of-the-art unsupervised approaches and achieves performance comparable to that of supervised methods. We hope that our approach will shed new light on HSI restoration and broader scientific imaging scenarios. The code will be released at this https URL.
Abstract (translated)
高光谱图像（HSI）恢复是计算成像和计算机视觉领域的一个基本挑战。它涉及到如插值和超分辨率之类的病态逆问题。尽管深度学习方法通过数据驱动的学习极大地改变了该领域，但它们的有效性依赖于对精心策划的地面实况数据集的访问。这种根本性的限制阻碍了这些方法在现实世界场景中的应用，因为在这些场景中通常没有可用的数据。本文提出了SHARE（具有等变性的单高光谱图像恢复），这是一种完全无监督的框架，它将几何等变性原理与低秩光谱建模相结合，以消除对地面实况数据的需求。 SHARE的核心概念是利用在可微分几何变换（例如旋转和缩放）下的高光谱结构固有的不变性，通过等价一致性约束来推导自监督信号。我们的新颖的动态自适应光谱注意（DASA）模块进一步增强了这一范式转变，该模块明确地编码了HSI的整体低秩属性，并通过可学习的关注机制自适应地细化局部光谱-空间相关性。 在HSI插值和超分辨率任务上的大量实验展示了SHARE的有效性。我们的方法优于许多最先进的无监督方法，在性能上与有监督的方法相当。我们希望我们的方法将为高光谱图像恢复以及更广泛的科学成像场景带来新的见解。代码将在[此链接](https://example.com)发布。
URL
https://arxiv.org/abs/2601.13987
PDF
-
SSPFormer: Self-Supervised Pretrained Transformer for MRI Images
2026-01-19 06:01:13Jingkai Li, Xiaoze Tian, Yuhang Shen, Jia Wang, Dianjie Lu, Guijuan Zhang, Zhuoran ZhengAbstract
The pre-trained transformer demonstrates remarkable generalization ability in natural image processing. However, directly transferring it to magnetic resonance images faces two key challenges: the inability to adapt to the specificity of medical anatomical structures and the limitations brought about by the privacy and scarcity of medical data. To address these issues, this paper proposes a Self-Supervised Pretrained Transformer (SSPFormer) for MRI images, which effectively learns domain-specific feature representations of medical images by leveraging unlabeled raw imaging data. To tackle the domain gap and data scarcity, we introduce inverse frequency projection masking, which prioritizes the reconstruction of high-frequency anatomical regions to enforce structure-aware representation learning. Simultaneously, to enhance robustness against real-world MRI artifacts, we employ frequency-weighted FFT noise enhancement that injects physiologically realistic noise into the Fourier domain. Together, these strategies enable the model to learn domain-invariant and artifact-robust features directly from raw scans. Through extensive experiments on segmentation, super-resolution, and denoising tasks, the proposed SSPFormer achieves state-of-the-art performance, fully verifying its ability to capture fine-grained MRI image fidelity and adapt to clinical application requirements.
Abstract (translated)
预训练的变压器在自然图像处理中展示了卓越的泛化能力。然而，直接将其应用于磁共振成像（MRI）时会面临两个关键挑战：一是无法适应医学解剖结构的特定性；二是由于医疗数据隐私性和稀缺性的限制带来的问题。为了解决这些问题，本文提出了一种用于MRI图像的自我监督预训练变压器（SSPFormer），该模型能够通过利用未标记的原始影像数据有效学习医学图像特有的特征表示。 为了应对领域差异和数据稀缺的问题，我们引入了逆频率投影掩码技术，优先重建高频率解剖区域，以此强化结构感知型表征学习。同时，为提高对现实世界MRI伪影的鲁棒性，我们采用了频率加权FFT噪声增强技术，在傅立叶域中注入生理真实的噪声。 通过这些策略，模型能够直接从原始扫描数据中学习到领域不变性和抗伪影特征。在分割、超分辨率和去噪任务上的广泛实验表明，所提出的SSPFormer达到了最先进的性能水平，充分验证了其捕捉精细MRI图像保真度及适应临床应用需求的能力。
URL
https://arxiv.org/abs/2601.12747
PDF
-
SDCoNet: Saliency-Driven Multi-Task Collaborative Network for Remote Sensing Object Detection
2026-01-18 17:36:48Ruo Qi, Linhui Dai, Yusong Qin, Chaolei Yang, Yanshan LiAbstract
In remote sensing images, complex backgrounds, weak object signals, and small object scales make accurate detection particularly challenging, especially under low-quality imaging conditions. A common strategy is to integrate single-image super-resolution (SR) before detection; however, such serial pipelines often suffer from misaligned optimization objectives, feature redundancy, and a lack of effective interaction between SR and detection. To address these issues, we propose a Saliency-Driven multi-task Collaborative Network (SDCoNet) that couples SR and detection through implicit feature sharing while preserving task specificity. SDCoNet employs the swin transformer-based shared encoder, where hierarchical window-shifted self-attention supports cross-task feature collaboration and adaptively balances the trade-off between texture refinement and semantic representation. In addition, a multi-scale saliency prediction module produces importance scores to select key tokens, enabling focused attention on weak object regions, suppression of background clutter, and suppression of adverse features introduced by multi-task coupling. Furthermore, a gradient routing strategy is introduced to mitigate optimization conflicts. It first stabilizes detection semantics and subsequently routes SR gradients along a detection-oriented direction, enabling the framework to guide the SR branch to generate high-frequency details that are explicitly beneficial for detection. Experiments on public datasets, including NWPU VHR-10-Split, DOTAv1.5-Split, and HRSSD-Split, demonstrate that the proposed method, while maintaining competitive computational efficiency, significantly outperforms existing mainstream algorithms in small object detection on low-quality remote sensing images. Our code is available at this https URL.
Abstract (translated)
在遥感图像中，复杂的背景、弱信号目标和小规模物体使得准确检测变得特别具有挑战性，尤其是在成像质量较低的情况下。一种常见的策略是在检测之前整合单幅图像的超分辨率（SR）处理；然而，这种串联管道通常会面临优化目标不一致、特征冗余以及超分辨率与检测之间缺乏有效交互的问题。为了解决这些问题，我们提出了一种以显著性为导向的多任务协作网络 (SDCoNet)，该网络通过隐式的特征共享将超分辨率和检测结合起来，同时保持各自任务的独特性。SDCoNet 使用基于 Swin 变换器的共享编码器，在此编码器中层次化的窗口偏移自注意力支持跨任务特性合作，并适应性地平衡纹理细化与语义表示之间的权衡。 此外，一个多尺度显著性预测模块生成重要度评分以选择关键令牌，从而能够对弱目标区域集中注意，抑制背景杂乱并压制由多任务结合引入的不利特征。而且，我们还提出了一种梯度路由策略来缓解优化冲突。该策略首先稳定检测语义，并随后将超分辨率梯度沿有利于检测的方向进行路由，使框架引导超分辨率分支生成对检测显式有益的高频细节。 在包括 NWPU VHR-10-Split、DOTAv1.5-Split 和 HRSSD-Split 在内的公共数据集上的实验表明，所提出的方法在低质量遥感图像的小目标检测方面显著优于现有的主流算法，同时保持了具有竞争力的计算效率。我们的代码可在 [这里](https://this https URL) 获取。
URL
https://arxiv.org/abs/2601.12507
PDF
-
SERA-H: Beyond Native Sentinel Spatial Limits for High-Resolution Canopy Height Mapping
2026-01-15 11:19:00Thomas Boudras, Martin Schwartz, Rasmus Fensholt, Martin Brandt, Ibrahim Fayad, Jean-Pierre Wigneron, Gabriel Belouze, Fajwel Fogel, Philippe CiaisAbstract
High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imagery to predict height maps, these approaches often face a trade-off between data accessibility and spatial resolution. To overcome these limitations, we present SERA-H, an end-to-end model combining a super-resolution module (EDSR) and temporal attention encoding (UTAE). Trained under the supervision of high-density LiDAR data (ALS), our model generates 2.5 m resolution height maps from freely available Sentinel-1 and Sentinel-2 (10 m) time series data. Evaluated on an open-source benchmark dataset in France, SERA-H, with a MAE of 2.6 m and a coefficient of determination of 0.82, not only outperforms standard Sentinel-1/2 baselines but also achieves performance comparable to or better than methods relying on commercial very high-resolution imagery (SPOT-6/7, PlanetScope, Maxar). These results demonstrate that combining high-resolution supervision with the spatiotemporal information embedded in time series enables the reconstruction of details beyond the input sensors' native resolution. SERA-H opens the possibility of freely mapping forests with high revisit frequency, achieving accuracy comparable to that of costly commercial imagery.
Abstract (translated)
高分辨率的树冠高度测绘对于森林管理和生物多样性监测至关重要。尽管最近的研究已导致使用卫星图像进行深度学习方法以预测高度图谱的发展，但这些方法通常在数据可访问性和空间分辨率之间存在权衡。为了解决这些问题，我们提出了SERA-H模型，这是一种结合了超分辨率模块（EDSR）和时间注意力编码（UTAE）的端到端模型。我们的模型在高密度激光雷达数据（ALS）的监督下训练，并能从免费提供的Sentinel-1和Sentinel-2（10米）时序数据生成2.5米分辨率的高度图。 在一个法国公开源代码基准数据集上进行评估，SERA-H模型具有2.6米的均方根误差(MAE) 和 0.82 的决定系数，在性能上不仅超越了标准Sentinel-1/2基线方法，也达到了与依赖于商业高分辨率图像（SPOT-6/7、PlanetScope和Maxar）的方法相当或更好的水平。这些结果表明，结合高分辨率监督以及时序数据中嵌入的时空信息可以重建超出输入传感器原生分辨率的细节。 SERA-H模型开启了以低成本实现高频重访的自由森林测绘的可能性，并且其准确性可与昂贵的商业图像相媲美。
URL
https://arxiv.org/abs/2512.18128
PDF
