Super_Resolution
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SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation
2026-03-17 17:59:30Jiongze Yu, Xiangbo Gao, Pooja Verlani, Akshay Gadde, Yilin Wang, Balu Adsumilli, Zhengzhong TuAbstract
Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces. In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal. Specifically, users can first super-resolve or optionally a small set of keyframes using any off-the-shelf image super-resolution (ISR) model, then SparkVSR propagates the keyframe priors to the entire video sequence while remaining grounded by the original LR video motion. Concretely, we introduce a keyframe-conditioned latent-pixel two-stage training pipeline that fuses LR video latents with sparsely encoded HR keyframe latents to learn robust cross-space propagation and refine perceptual details. At inference time, SparkVSR supports flexible keyframe selection (manual specification, codec I-frame extraction, or random sampling) and a reference-free guidance mechanism that continuously balances keyframe adherence and blind restoration, ensuring robust performance even when reference keyframes are absent or imperfect. Experiments on multiple VSR benchmarks demonstrate improved temporal consistency and strong restoration quality, surpassing baselines by up to 24.6%, 21.8%, and 5.6% on CLIP-IQA, DOVER, and MUSIQ, respectively, enabling controllable, keyframe-driven video super-resolution. Moreover, we demonstrate that SparkVSR is a generic interactive, keyframe-conditioned video processing framework as it can be applied out of the box to unseen tasks such as old-film restoration and video style transfer. Our project page is available at: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2603.16864
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RASLF: Representation-Aware State Space Model for Light Field Super-Resolution
2026-03-17 08:28:00Zeqiang Wei, Kai Jin, Kuan Song, Xiuzhuang Zhou, Wenlong Chen, Min XuAbstract
Current SSM-based light field super-resolution (LFSR) methods often fail to fully leverage the complementarity among various LF representations, leading to the loss of fine textures and geometric misalignments across views. To address these issues, we propose RASLF, a representation-aware state-space framework that explicitly models structural correlations across multiple LF representations. Specifically, a Progressive Geometric Refinement (PGR) block is created that uses a panoramic epipolar representation to explicitly encode multi-view parallax differences, thereby enabling integration across different LF representations. Furthermore, we introduce a Representation Aware Asymmetric Scanning (RAAS) mechanism that dynamically adjusts scanning paths based on the physical properties of different representation spaces, optimizing the balance between performance and efficiency through path pruning. Additionally, a Dual-Anchor Aggregation (DAA) module improves hierarchical feature flow, reducing redundant deeplayer features and prioritizing important reconstruction information. Experiments on various public benchmarks show that RASLF achieves the highest reconstruction accuracy while remaining highly computationally efficient.
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URL
https://arxiv.org/abs/2603.16243
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GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution
2026-03-16 15:24:39Qiaosi Yi, Shuai Li, Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang, Lei ZhangAbstract
Recently, reinforcement learning (RL) has been employed for improving generative image super-resolution (ISR) performance. However, the current efforts are focused on multi-step generative ISR, while one-step generative ISR remains underexplored due to its limited stochasticity. In addition, RL methods such as Direct Preference Optimization (DPO) require the generation of positive and negative sample pairs offline, leading to a limited number of samples, while Group Relative Policy Optimization (GRPO) only calculates the likelihood of the entire image, ignoring local details that are crucial for ISR. In this paper, we propose Group Direct Preference Optimization (GDPO), a novel approach to integrate RL into one-step generative ISR model training. First, we introduce a noise-aware one-step diffusion model that can generate diverse ISR outputs. To prevent performance degradation caused by noise injection, we introduce an unequal-timestep strategy to decouple the timestep of noise addition from that of diffusion. We then present the GDPO strategy, which integrates the principle of GRPO into DPO, to calculate the group-relative advantage of each online generated sample for model optimization. Meanwhile, an attribute-aware reward function is designed to dynamically evaluate the score of each sample based on its statistics of smooth and texture areas. Experiments demonstrate the effectiveness of GDPO in enhancing the performance of one-step generative ISR models. Code: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16769
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TextOVSR: Text-Guided Real-World Opera Video Super-Resolution
2026-03-16 11:48:43Hua Chang, Xin Xu, Wei Liu, Jiayi Wu, Kui Jiang, Fei Ma, Qi TianAbstract
Many classic opera videos exhibit poor visual quality due to the limitations of early filming equipment and long-term degradation during storage. Although real-world video super-resolution (RWVSR) has achieved significant advances in recent years, directly applying existing methods to degraded opera videos remains challenging. The difficulties are twofold. First, accurately modeling real-world degradations is complex: simplistic combinations of classical degradation kernels fail to capture the authentic noise distribution, while methods that extract real noise patches from external datasets are prone to style mismatches that introduce visual artifacts. Second, current RWVSR methods, which rely solely on degraded image features, struggle to reconstruct realistic and detailed textures due to a lack of high-level semantic guidance. To address these issues, we propose a Text-guided Dual-Branch Opera Video Super-Resolution (TextOVSR) network, which introduces two types of textual prompts to guide the super-resolution process. Specifically, degradation-descriptive text, derived from the degradation process, is incorporated into the negative branch to constrain the solution space. Simultaneously, content-descriptive text is incorporated into a positive branch and our proposed Text-Enhanced Discriminator (TED) to provide semantic guidance for enhanced texture reconstruction. Furthermore, we design a Degradation-Robust Feature Fusion (DRF) module to facilitate cross-modal feature fusion while suppressing degradation interference. Experiments on our OperaLQ benchmark show that TextOVSR outperforms state-of-the-art methods both qualitatively and quantitatively. The code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15153
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Comparative Analysis of 3D Convolutional and 2.5D Slice-Conditioned U-Net Architectures for MRI Super-Resolution via Elucidated Diffusion Models
2026-03-15 23:48:29Hendrik Chiche, Ludovic Corcos, Logan RougeAbstract
Magnetic resonance imaging (MRI) super-resolution (SR) methods that computationally enhance low-resolution acquisitions to approximate high-resolution quality offer a compelling alternative to expensive high-field scanners. In this work we investigate an elucidated diffusion model (EDM) framework for brain MRI SR and compare two U-Net backbone architectures: (i) a full 3D convolutional U-Net that processes volumetric patches with 3D convolutions and multi-head self-attention, and (ii) a 2.5D slice-conditioned U-Net that super-resolves each slice independently while conditioning on an adjacent slice for inter-slice context. Both models employ continuous-sigma noise conditioning following Karras et al. and are trained on the NKI cohort of the FOMO60K dataset. On a held-out test set of 5 subjects (6 volumes, 993 slices), the 3D model achieves 37.75 dB PSNR, 0.997 SSIM, and 0.020 LPIPS, improving on the off-the-shelf pretrained EDSR baseline (35.57 dB / 0.024 LPIPS) and the 2.5D variant (35.82 dB) across all three metrics under the same test data and degradation pipeline.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14667
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High-Fidelity Compression of Seismic Velocity Models via SIREN Auto-Decoders
2026-03-15 08:43:39Caiyun Liu, Xiaoxue Luo, Jie XiongAbstract
Implicit Neural Representations (INRs) have emerged as a powerful paradigm for representing continuous signals independently of grid resolution. In this paper, we propose a high-fidelity neural compression framework based on a SIREN (Sinusoidal Representation Networks) auto-decoder to represent multi-structural seismic velocity models from the OpenFWI benchmark. Our method compresses each 70x70 velocity map (4,900 points) into a compact 256-dimensional latent vector, achieving a compression ratio of 19:1. We evaluate the framework on 1,000 samples across five diverse geological families: FlatVel, CurveVel, FlatFault, CurveFault, and Style. Experimental results demonstrate an average PSNR of 32.47 dB and SSIM of 0.956, indicating high-quality reconstruction. Furthermore, we showcase two key advantages of our implicit representation: (1) smooth latent space interpolation that generates plausible intermediate velocity structures, and (2) zero-shot super-resolution capability that reconstructs velocity fields at arbitrary resolutions up to 280x280 without additional training. The results highlight the potential of INR-based auto-decoders for efficient storage, multi-scale analysis, and downstream geophysical applications such as full waveform inversion.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14284
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DualTSR: Unified Dual-Diffusion Transformer for Scene Text Image Super-Resolution
2026-03-15 03:50:47Axi Niu, Kang Zhang, Qingsen Yan, Hao Jin, Jinqiu Sun, Yanning ZhangAbstract
Scene Text Image Super-Resolution (STISR) aims to restore high-resolution details in low-resolution text images, which is crucial for both human readability and machine recognition. Existing methods, however, often depend on external Optical Character Recognition (OCR) models for textual priors or rely on complex multi-component architectures that are difficult to train and reproduce. In this paper, we introduce DualTSR, a unified end-to-end framework that addresses both issues. DualTSR employs a single multimodal transformer backbone trained with a dual diffusion objective. It simultaneously models the continuous distribution of high-resolution images via Conditional Flow Matching and the discrete distribution of textual content via discrete diffusion. This shared design enables visual and textual information to interact at every layer, allowing the model to infer text priors internally instead of relying on an external OCR module. Compared with prior multi-branch diffusion systems, DualTSR offers a simpler end-to-end formulation with fewer hand-crafted components. Experiments on synthetic Chinese benchmarks and a curated real-world evaluation protocol show that DualTSR achieves strong perceptual quality and text fidelity.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14207
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Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
2026-03-14 21:25:46Daniel Tweneboah Anyimadu (Department of Computer Science, University of Exeter, United Kingdom), Mohammed Abdalla (Neurology Department, Royal Devon and Exeter Hospital, Exeter, United Kingdom), Mohammed M. Abdelsamea (Department of Computer Science, University of Exeter, United Kingdom), Ahmed Karam Eldaly (Department of Computer Science, University of Exeter, United Kingdom, UCL Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom)Abstract
Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two key challenges: prolonged scan times and reduced image quality. Accelerated acquisition can be achieved using k-space undersampling, while image enhancement traditionally relies on spatial-domain postprocessing. In this work, we propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images directly using undersampled data while quantifying the impact of reduced k-space sampling. Unlike conventional approaches that treat image super-resolution as a postprocessing step following image reconstruction from undersampled k-space, our unified model integrates both processes, leveraging k-space information to achieve superior image fidelity. Extensive experiments on synthetic and real low-field brain MRI datasets demonstrate that k-space-driven image super-resolution outperforms conventional spatial-domain counterparts. Furthermore, our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions, enabling substantial scan-time acceleration without compromising diagnostic utility.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14125
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Revisiting the Perception-Distortion Trade-off with Spatial-Semantic Guided Super-Resolution
2026-03-14 20:41:27Dan Wang, Haiyan Sun, Shan Du, Z. Jane Wang, Zhaochong An, Serge Belongie, Xinrui CuiAbstract
Image super-resolution (SR) aims to reconstruct high resolution images with both high perceptual quality and low distortion, but is fundamentally limited by the perception-distortion trade-off. GAN-based SR methods reduce distortion but still struggle with realistic fine-grained textures, whereas diffusion-based approaches synthesize rich details but often deviate from the input, hallucinating structures and degrading fidelity. This tension raises a key challenge: how to exploit the powerful generative priors of diffusion models without sacrificing fidelity. To address this, we propose SpaSemSR, a spatial-semantic guided diffusion framework with two complementary guidances. First, spatial-grounded textual guidance integrates object-level spatial cues with semantic prompts, aligning textual and visual structures to reduce distortion. Second, semantic-enhanced visual guidance with a multi-encoder design and semantic degradation constraints unifies multimodal semantic priors, improving perceptual realism under severe degradations. These complementary guidances are adaptively fused into the diffusion process via spatial-semantic attention, suppressing distortion and hallucination while retaining the strengths of diffusion models. Extensive experiments on multiple benchmarks show that SpaSemSR achieves a superior perception-distortion balance, producing both realistic and faithful restorations.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14112
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Self-Supervised Uncertainty Estimation For Super-Resolution of Satellite Images
2026-03-14 18:41:43Zhe Zheng, Val\'ery Dewil, Pablo AriasAbstract
Super-resolution (SR) of satellite imagery is challenging due to the lack of paired low-/high-resolution data. Recent self-supervised SR methods overcome this limitation by exploiting the temporal redundancy in burst observations, but they lack a mechanism to quantify uncertainty in the reconstruction. In this work, we introduce a novel self-supervised loss that allows to estimate uncertainty in image super-resolution without ever accessing the ground-truth high-resolution data. We adopt a decision-theoretic perspective and show that minimizing the corresponding Bayesian risk yields the posterior mean and variance as optimal estimators. We validate our approach on a synthetic SkySat L1B dataset and demonstrate that it produces calibrated uncertainty estimates comparable to supervised methods. Our work bridges self-supervised restoration with uncertainty quantification, making a practical framework for uncertainty-aware image reconstruction.
Abstract (translated)
URL
https://arxiv.org/abs/2603.14074
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CT-Conditioned Diffusion Prior with Physics-Constrained Sampling for PET Super-Resolution
2026-03-14 11:28:23Liutao Yang, Zi Wang, Peiyuan Jing, Xiaowen Wang, Javier A. Montoya-Zegarra, Kuangyu Shi, Daoqiang Zhang, Guang YangAbstract
PET super-resolution is highly under-constrained because paired multi-resolution scans from the same subject are rarely available, and effective resolution is determined by scanner-specific physics (e.g., PSF, detector geometry, and acquisition settings). This limits supervised end-to-end training and makes purely image-domain generative restoration prone to hallucinated structures when anatomical and physical constraints are weak. We formulate PET super-resolution as posterior inference under heterogeneous system configurations and propose a CT-conditioned diffusion framework with physics-constrained sampling. During training, a conditional diffusion prior is learned from high-quality PET/CT pairs using cross-attention for anatomical guidance, without requiring paired LR--HR PET data. During inference, measurement consistency is enforced through a scanner-aware forward model with explicit PSF effects and gradient-based data-consistency refinement. Under both standard and OOD settings, the proposed method consistently improves experimental metrics and lesion-level clinical relevance indicators over strong baselines, while reducing hallucination artifacts and improving structural fidelity.
Abstract (translated)
URL
https://arxiv.org/abs/2603.13901
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OARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution
2026-03-13 09:12:46Shijie Zhao, Xuanyu Zhang, Bin Chen, Weiqi Li, Qunliang Xing, Kexin Zhang, Yan Wang, Junlin Li, Li Zhang, Jian Zhang, Tianfan XueAbstract
Aligning generative real-world image super-resolution models with human visual preference is challenging due to the perception--fidelity trade-off and diverse, unknown degradations. Prior approaches rely on offline preference optimization and static metric aggregation, which are often non-interpretable and prone to pseudo-diversity under strong conditioning. We propose OARS, a process-aware online alignment framework built on COMPASS, a MLLM-based reward that evaluates the LR to SR transition by jointly modeling fidelity preservation and perceptual gain with an input-quality-adaptive trade-off. To train COMPASS, we curate COMPASS-20K spanning synthetic and real degradations, and introduce a three-stage perceptual annotation pipeline that yields calibrated, fine-grained training labels. Guided by COMPASS, OARS performs progressive online alignment from cold-start flow matching to full-reference and finally reference-free RL via shallow LoRA optimization for on-policy exploration. Extensive experiments and user studies demonstrate consistent perceptual improvements while maintaining fidelity, achieving state-of-the-art performance on Real-ISR benchmarks.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12811
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RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution
2026-03-12 22:22:55Ali Mosleh, Faraz Ali, Fengjia Zhang, Stavros Tsogkas, Junyong Lee, Alex Levinshtein, Michael S. BrownAbstract
Digital zoom on smartphones relies on learning-based super-resolution (SR) models that operate on RAW sensor images, but obtaining sensor-specific training data is challenging due to the lack of ground-truth images. Synthetic data generation via ``unprocessing'' pipelines offers a potential solution by simulating the degradations that transform high-resolution (HR) images into their low-resolution (LR) counterparts. However, these pipelines can introduce domain gaps due to incomplete or unrealistic degradation modeling. In this paper, we demonstrate that principled and carefully designed degradation modeling can enhance SR performance in real-world conditions. Instead of relying on generic priors for camera blur and noise, we model device-specific degradations through calibration and unprocess publicly available rendered images into the RAW domain of different smartphones. Using these image pairs, we train a single-image RAW-to-RGB SR model and evaluate it on real data from a held-out device. Our experiments show that accurate degradation modeling leads to noticeable improvements, with our SR model outperforming baselines trained on large pools of arbitrarily chosen degradations.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12493
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UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution
2026-03-12 08:46:19Cao Thien Tan, Phan Thi Thu Trang, Do Nghiem Duc, Ho Ngoc Anh, Hanyang Zhuang, Nguyen Duc DungAbstract
Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to expand the effective receptive field efficiently. UCAN combines window-based spatial attention with a Hedgehog Attention mechanism to model both local texture and long-range dependencies, and introduces a distillation-based large-kernel module to preserve high-frequency structure without heavy computation. In addition, we employ cross-layer parameter sharing to further reduce complexity. On Manga109 ($4\times$), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models. Extensive experiments show that UCAN achieves a superior trade-off between accuracy, efficiency, and scalability, making it well-suited for practical high-resolution image restoration.
Abstract (translated)
混合CNN-Transformer架构在图像超分辨率任务中取得了很好的结果，但扩大注意力窗口或卷积核会显著增加计算成本，从而限制了其在资源受限设备上的部署。我们提出了UCAN（Unified Convolution and Attention Network），这是一种轻量级网络，它将卷积和注意力统一起来以高效地扩展有效感受野。 UCAN结合了基于窗口的空间注意机制与刺猬注意力机制，以便同时建模局部纹理和长程依赖关系，并引入了一种基于知识蒸馏的大核模块来在不进行大量计算的情况下保留高频结构。此外，我们还采用了跨层参数共享策略进一步降低复杂度。 在Manga109（4倍超分辨率）数据集上，UCAN-L实现了31.63 dB的PSNR值，仅使用了48.4G MACs的操作量，超过了最近的一些轻量级模型。而在BSDS100数据集上，UCAN达到了27.79 dB的成绩，优于那些拥有显著更大规模模型的方法。 通过广泛的实验显示，UCAN在精度、效率和可扩展性之间实现了更佳的平衡，使其非常适合用于实际高分辨率图像恢复任务中。
URL
https://arxiv.org/abs/2603.11680
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CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy
2026-03-10 16:05:21Su Yan, Clara Rodrigo Gonzalez, Vincent C. H. Leung, Herman Verinaz-Jadan, Jiakang Chen, Matthieu Toulemonde, Kai Riemer, Jipeng Yan, Clotilde Vi\'e, Qingyuan Tan, Peter D. Weinberg, Pier Luigi Dragotti, Kevin G. Murphy, Meng-Xing TangAbstract
Super-resolution ultrasound via microbubble (MB) localisation and tracking, also known as ultrasound localisation microscopy (ULM), can resolve microvasculature beyond the acoustic diffraction limit. However, significant challenges remain in localisation performance and data acquisition and processing time. Deep learning methods for ULM have shown promise to address these challenges, however, they remain limited by in vivo label scarcity and the simulation-to-reality domain gap. We present CycleULM, the first unified label-free deep learning framework for ULM. CycleULM learns a physics-emulating translation between the real contrast-enhanced ultrasound (CEUS) data domain and a simplified MB-only domain, leveraging the power of CycleGAN without requiring paired ground truth data. With this translation, CycleULM removes dependence on high-fidelity simulators or labelled data, and makes MB localisation and tracking substantially easier. Deployed as modular plug-and-play components within existing pipelines or as an end-to-end processing framework, CycleULM delivers substantial performance gains across both in silico and in vivo datasets. Specifically, CycleULM improves image contrast (contrast-to-noise ratio) by up to 15.3 dB and sharpens CEUS resolution with a 2.5{\times} reduction in the full width at half maximum of the point spread function. CycleULM also improves MB localisation performance, with up to +40% recall, +46% precision, and a -14.0 {\mu}m mean localisation error, yielding more faithful vascular reconstructions. Importantly, CycleULM achieves real-time processing throughput at 18.3 frames per second with order-of-magnitude speed-ups (up to ~14.5{\times}). By combining label-free learning, performance enhancement, and computational efficiency, CycleULM provides a practical pathway toward robust, real-time ULM and accelerates its translation to clinical applications.
Abstract (translated)
通过微泡（MB）定位和跟踪的超分辨率超声波，也称为超声定位显微镜（ULM），能够解析出超出声学衍射极限的微血管结构。然而，在定位性能和数据采集及处理时间方面仍然存在重大挑战。基于深度学习的方法在解决这些挑战方面显示出巨大潜力，但它们仍受限于体内标签稀缺以及仿真与现实场景之间的领域差距。我们提出了CycleULM，这是第一个统一的无标记深度学习框架用于超声定位显微镜（ULM）。 CycleULM 学习了一个仿物理现象的真实对比增强超声波（CEUS）数据域和简化后的MB仅存在域之间的转换，利用了CycleGAN的力量而不需配对的真实标签数据。通过这种转换，CycleULM消除了对高保真模拟器或标记数据的依赖，并使MB定位和跟踪变得更加容易。 作为现有流水线中的模块化即插即用组件部署或者作为一个端到端处理框架，CycleULM在基于仿真和体内数据集上都实现了显著性能提升。具体来说，CycleULM将图像对比度提高了高达15.3 dB（信噪比），并将CEUS分辨率提高了一半最大全宽的两倍半，这意味着点扩散函数的宽度减小了2.5倍。 此外，CycleULM改进了MB定位性能，召回率提高了多达40%，精度提高了46%，平均定位误差减少了14.0 μm，从而提供了更加忠实的血管重建。更重要的是，CycleULM实现了每秒18.3帧的真实时间处理吞吐量，并达到了数量级的速度提升（最高可达约14.5倍）。通过结合无标签学习、性能增强和计算效率，CycleULM为稳健且实时的超声定位显微镜提供了实用途径，并加速了其向临床应用的转化。
URL
https://arxiv.org/abs/2603.09840
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TriFusion-SR: Joint Tri-Modal Medical Image Fusion and SR
2026-03-10 14:13:40Fayaz Ali Dharejo, Sharif S. M. A., Aiman Khalil, Nachiket Chaudhary, Rizwan Ali Naqvi, Radu TimofteAbstract
Multimodal medical image fusion facilitates comprehensive diagnosis by aggregating complementary structural and functional information, but its effectiveness is limited by resolution degradation and modality discrepancies. Existing approaches typically perform image fusion and super-resolution (SR) in separate stages, leading to artifacts and degraded perceptual quality. These limitations are further amplified in tri-modal settings that combine anatomical modalities (e.g., MRI, CT) with functional scans (e.g., PET, SPECT) due to pronounced frequency domain imbalances. We propose TriFusionSR, a wavelet-guided conditional diffusion framework for joint tri-modal fusion and SR. The framework explicitly decomposes multimodal features into frequency bands using the 2D Discrete Wavelet Transform, enabling frequency-aware crossmodal interaction. We further introduce a Rectified Wavelet Features (RWF) strategy for latent coefficient calibration, followed by an Adaptive Spatial-Frequency Fusion (ASFF) module with gated channel-spatial attention to enable structure-driven multimodal refinement. Extensive experiments demonstrate state-of-the-art performance, achieving 4.8-12.4% PSNR improvement and substantial reductions in RMSE and LPIPS across multiple upsampling scales.
Abstract (translated)
多模态医学图像融合通过聚合互补的结构和功能信息，促进了全面诊断。然而，其有效性受到分辨率退化和模式差异的限制。现有方法通常在单独阶段执行图像融合和超分辨率(SR)，这导致了伪影和感知质量下降。在结合解剖学模式（如MRI、CT）与功能性扫描（如PET、SPECT）的三模态设置中，这些局限性由于频域不平衡而进一步加剧。 我们提出了TriFusionSR，这是一种基于小波指导的条件扩散框架，用于联合执行三模态融合和超分辨率。该框架明确地使用二维离散小波变换将多模式特征分解为频率带，从而实现频率感知的跨模态交互。我们还引入了一种修正小波特征（RWF）策略，用于潜在系数校准，并随后采用了一个带有门控通道空间注意机制的自适应空域频域融合（ASFF）模块，以实现结构驱动的多模式细化。 广泛的实验展示了该方法在多个上采样尺度下的优越性能，实现了PSNR 4.8-12.4%的提升，并大幅减少了RMSE和LPIPS误差。
URL
https://arxiv.org/abs/2603.09702
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Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution
2026-03-10 13:00:42Shuting Liu, Lei Zhang, Wei Huang, Zhao Zhang, Zizhou WangAbstract
High-resolution Magnetic Resonance Imaging (MRI) is vital for clinical diagnosis but limited by long acquisition times and motion artifacts. Super-resolution (SR) reconstructs low-resolution scans into high-resolution images, yet existing methods are mutually constrained: paired-data methods achieve efficiency only by relying on costly aligned datasets, while implicit neural representation approaches avoid such data needs at the expense of heavy computation. We propose a zero-shot MRI SR framework using explicit Gaussian representation to balance data requirements and efficiency. MRI-tailored Gaussian parameters embed tissue physical properties, reducing learnable parameters while preserving MR signal fidelity. A physics-grounded volume rendering strategy models MRI signal formation via normalized Gaussian aggregation. Additionally, a brick-based order-independent rasterization scheme enables highly parallel 3D computation, lowering training and inference costs. Experiments on two public MRI datasets show superior reconstruction quality and efficiency, demonstrating the method's potential for clinical MRI SR.
Abstract (translated)
高分辨率磁共振成像（MRI）对于临床诊断至关重要，但由于采集时间长和运动伪影的限制而受到制约。超分辨率（SR）技术可以将低分辨率扫描重建为高质量的高分辨率图像，但现有的方法在数据需求与效率之间存在着相互制约：基于配对数据的方法仅通过依赖昂贵且需精确对齐的数据集来实现高效性，而隐式的神经网络表示方法则避免了这样的数据需求，却牺牲了计算效率。我们提出了一种使用明确的高斯表示进行零样本MRI超分辨率重建框架，旨在平衡数据要求和效率之间的关系。 该框架中，针对MRI定制化的高斯参数嵌入了组织物理特性，减少了需要学习的参数数量，同时保持了磁共振信号的真实度。一种基于物理学原理的体积渲染策略通过归一化高斯聚合来模拟MRI信号形成过程。此外，还引入了一种基于砖块的与绘制顺序无关的光栅化方案，使得三维计算能够高度并行化，从而降低了训练和推理的成本。 在两个公开的MRI数据集上的实验表明，该方法具有卓越的重建质量和效率，展示了其应用于临床MRI超分辨率成像的巨大潜力。
URL
https://arxiv.org/abs/2603.09621
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Progressive Split Mamba: Effective State Space Modelling for Image Restoration
2026-03-10 04:19:52Mohammed Hassanin, Nour Moustafa, Weijian Deng, Ibrahim RadwanAbstract
Image restoration requires simultaneously preserving fine-grained local structures and maintaining long-range spatial coherence. While convolutional networks struggle with limited receptive fields, and Transformers incur quadratic complexity for global attention, recent State Space Models (SSMs), such as Mamba, provide an appealing linear-time alternative for long-range dependency modelling. However, naively extending Mamba to 2D images exposes two intrinsic shortcomings. First, flattening 2D feature maps into 1D sequences disrupts spatial topology, leading to locality distortion that hampers precise structural recovery. Second, the stability-driven recurrent dynamics of SSMs induce long-range decay, progressively attenuating information across distant spatial positions and weakening global consistency. Together, these effects limit the effectiveness of state-space modelling in high-fidelity restoration. We propose Progressive Split-Mamba (PS-Mamba), a topology-aware hierarchical state-space framework designed to reconcile locality preservation with efficient global propagation. Instead of sequentially flattening entire feature maps, PS-Mamba performs geometry-consistent partitioning, maintaining neighbourhood integrity prior to state-space processing. A progressive split hierarchy (halves, quadrants, octants) enables structured multi-scale modelling while retaining linear complexity. To counteract long-range decay, we introduce symmetric cross-scale shortcut pathways that directly transmit low-frequency global context across hierarchical levels, stabilising information flow over large spatial extents. Extensive experiments on super-resolution, denoising, and JPEG artifact reduction show consistent improvements over recent Mamba-based and attention-based models with a clear margin.
Abstract (translated)
图像恢复需要同时保留精细的局部结构和保持长距离的空间一致性。虽然卷积网络在受限的感受野方面存在困难，而Transformer在全局注意力机制下会因二次复杂度问题导致效率低下，最近的状态空间模型（如Mamba）提供了一种有吸引力的线性时间替代方案来处理长期依赖关系建模。然而，简单地将Mamba扩展到2D图像揭示了两个内在不足之处。首先，将2D特征图扁平化为1D序列会破坏空间拓扑结构，导致局部失真，阻碍精确结构恢复。其次，状态空间模型中的稳定性驱动递归动力学会导致长距离衰减，在远离的空间位置上逐渐减弱信息传递并削弱全局一致性。这些影响共同限制了高保真度图像恢复中状态空间建模的有效性。 为了解决上述问题，我们提出了渐进式分割Mamba（PS-Mamba），这是一种拓扑感知的分层状态空间框架，旨在平衡局部结构保留与高效全局传播之间的关系。不同于将整个特征图依次扁平化处理，PS-Mamba采用几何一致性的分区方法，在进入状态空间处理前保持邻域完整性。渐进式分割层次结构（半、四分之一和八分之一）能够在维护线性复杂度的同时支持结构化的多尺度建模。为了对抗长距离衰减问题，我们引入了对称跨尺度快捷通道，直接在不同层级之间传递低频全局上下文信息，从而稳定大范围空间的信息流动。 广泛的实验结果显示，在超分辨率、去噪和JPEG伪像消除方面，PS-Mamba相比于最近基于Mamba的模型以及注意力机制模型均表现出一致性的改进效果，并且优势明显。
URL
https://arxiv.org/abs/2603.09171
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POLISH'ing the Sky: Wide-Field and High-Dynamic Range Interferometric Image Reconstruction with Application to Strong Lens Discovery
2026-03-10 03:59:26Zihui Wu, Liam Connor, Samuel McCarty, Katherine L. BoumanAbstract
Radio interferometry enables high-resolution imaging of astronomical radio sources by synthesizing a large effective aperture from an array of antennas and solving a deconvolution problem to reconstruct the image. Deep learning has emerged as a promising solution to the imaging problem, reducing computational costs and enabling super-resolution. However, existing DL-based methods often fall short of the requirements for real-world deployment due to limitations in handling high dynamic range, large field of view, and mismatches between training and test conditions. In this work, we build upon and extend the POLISH framework, a recent DL model for radio interferometric imaging. We introduce key improvements to enable robust reconstruction and super-resolution under real-world conditions: (1) a patch-wise training and stitching strategy for scaling to wide-field imaging and (2) a nonlinear arcsinh-based intensity transformation to manage high dynamic range. We conduct comprehensive evaluations using the T-RECS simulation suite with realistic sky models and point spead functions (PSF), and demonstrate that our approach significantly improves reconstruction quality and robustness. We test the model on realistic simulated strong gravitational lenses and show that lens systems with Einstein radii near the PSF scale can be recovered after deconvolution with our POLISH model, potentially yielding 10$\times$ more galaxy-galaxy lensing systems from the Deep Synoptic Array (DSA) survey than with image-plane CLEAN. Our results highlight the potential of DL models as practical, scalable tools for next-generation radio astronomy.
Abstract (translated)
无线电干涉测量通过合成大型有效孔径并解决去卷积问题来实现对天文射电源的高分辨率成像，从而利用一组天线阵列。深度学习已成为解决成像问题的一项有前景的技术，它能降低计算成本，并支持超分辨率成像。然而，现有的基于深度学习的方法在处理高动态范围、大视场以及训练和测试条件不匹配等问题时往往表现不足，难以满足实际应用的要求。 在此工作中，我们建立并扩展了POLISH框架——这是最近提出的一种用于无线电干涉测量成像的深度学习模型。我们引入了一些关键改进，旨在实现在现实世界条件下稳健重建和超分辨率：（1）一种基于补丁训练和拼接策略的方法，以适应宽视野成像；（2）一种非线性的arcsinh强度变换方法来管理高动态范围。 使用带有真实天空模型和点扩散函数(PSF)的T-RECS模拟套件进行了全面评估。我们证明了我们的方法显著提高了重建质量和鲁棒性。我们在现实世界的强引力透镜模拟上测试了该模型，并展示了在去卷积后，利用我们的POLISH模型可以恢复出接近PSF尺度的爱因斯坦半径的透镜系统，从而有可能从深巡天阵列(DSA)调查中获得比使用图像平面CLEAN多10倍的星系-星系透镜系统。 我们的结果强调了深度学习模型作为下一代射电天文领域实用、可扩展工具的巨大潜力。
URL
https://arxiv.org/abs/2603.09162
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Rotation Equivariant Mamba for Vision Tasks
2026-03-10 03:22:33Zhongchen Zhao, Qi Xie, Keyu Huang, Lei Zhang, Deyu Meng, Zongben XuAbstract
Rotation equivariance constitutes one of the most general and crucial structural priors for visual data, yet it remains notably absent from current Mamba-based vision architectures. Despite the success of Mamba in natural language processing and its growing adoption in computer vision, existing visual Mamba models fail to account for rotational symmetry in their design. This omission renders them inherently sensitive to image rotations, thereby constraining their robustness and cross-task generalization. To address this limitation, we propose to incorporate rotation symmetry, a universal and fundamental geometric prior in images, into Mamba-based architectures. Specifically, we introduce EQ-VMamba, the first rotation equivariant visual Mamba architecture for vision tasks. The core components of EQ-VMamba include a carefully designed rotation equivariant cross-scan strategy and group Mamba blocks. Moreover, we provide a rigorous theoretical analysis of the intrinsic equivariance error, demonstrating that the proposed architecture enforces end-to-end rotation equivariance throughout the network. Extensive experiments across multiple benchmarks - including high-level image classification task, mid-level semantic segmentation task, and low-level image super-resolution task - demonstrate that EQ-VMamba achieves superior or competitive performance compared to non-equivariant baselines, while requiring approximately 50% fewer parameters. These results indicate that embedding rotation equivariance not only effectively bolsters the robustness of visual Mamba models against rotation transformations, but also enhances overall performance with significantly improved parameter efficiency. Code is available at this https URL.
Abstract (translated)
旋转等变性构成了视觉数据中最通用和最关键的结构先验之一，然而它在当前基于Mamba的视觉架构中却显著缺席。尽管Mamba在自然语言处理领域取得了成功，并且逐渐被应用于计算机视觉，现有的视觉Mamba模型在其设计中未能考虑旋转对称性。这种遗漏使它们内在地容易受到图像旋转的影响，从而限制了其鲁棒性和跨任务泛化能力。 为了解决这一局限性，我们提议将旋转对称性——一种普遍而基本的几何先验，在基于Mamba的架构中融入其中。具体而言，我们提出了EQ-VMamba，这是首个针对视觉任务的旋转等变视觉Mamba架构。EQ-VMamba的核心组件包括精心设计的旋转等变交叉扫描策略和群组Mamba块。此外，我们还提供了关于内在等变误差的严格理论分析，表明所提出的架构在整个网络中执行端到端的旋转等变性。 在多个基准测试——包括高层次图像分类任务、中层次语义分割任务和低层次图像超分辨率任务上的广泛实验显示，EQ-VMamba相较于非等变基线模型，不仅实现了优越或具有竞争力的表现，还减少了大约50%的参数需求。这些结果表明，在视觉Mamba模型中嵌入旋转等变性不仅能有效增强其对旋转变换的鲁棒性，还能通过显著提高参数效率来提升整体性能。 代码可在此链接获取：[提供URL的地方]（请确保替换为实际可用的有效链接）。
URL
https://arxiv.org/abs/2603.09138
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