Restoration
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UCMNet: Uncertainty-Aware Context Memory Network for Under-Display Camera Image Restoration
2026-04-01 02:05:31Daehyun Kim, Youngmin Kim, Yoon Ju Oh, Tae Hyun KimAbstract
Under-display cameras (UDCs) allow for full-screen designs by positioning the imaging sensor underneath the display. Nonetheless, light diffraction and scattering through the various display layers result in spatially varying and complex degradations, which significantly reduce high-frequency details. Current PSF-based physical modeling techniques and frequency-separation networks are effective at reconstructing low-frequency structures and maintaining overall color consistency. However, they still face challenges in recovering fine details when dealing with complex, spatially varying degradation. To solve this problem, we propose a lightweight \textbf{U}ncertainty-aware \textbf{C}ontext-\textbf{M}emory \textbf{Network} (\textbf{UCMNet}), for UDC image restoration. Unlike previous methods that apply uniform restoration, UCMNet performs uncertainty-aware adaptive processing to restore high-frequency details in regions with varying degradations. The estimated uncertainty maps, learned through an uncertainty-driven loss, quantify spatial uncertainty induced by diffraction and scattering, and guide the Memory Bank to retrieve region-adaptive context from the Context Bank. This process enables effective modeling of the non-uniform degradation characteristics inherent to UDC imaging. Leveraging this uncertainty as a prior, UCMNet achieves state-of-the-art performance on multiple benchmarks with 30\% fewer parameters than previous models. Project page: \href{this https URL}{this https URL}.
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URL
https://arxiv.org/abs/2604.00381
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Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration
2026-03-31 14:13:10Fengyang Xiao, Peng Hu, Lei Xu, XingE Guo, Guanyi Qin, Yuqi Shen, Chengyu Fang, Rihan Zhang, Chunming He, Sina FarsiuAbstract
Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depend on ground-truth (GT) supervision, assuming that GT provides perfect reference quality. However, GT can still contain images with inconsistent perceptual fidelity, causing models to converge to the average quality level of the training data rather than achieving the highest perceptual quality attainable. To address these problems, we propose a novel framework, termed IQPIR, that introduces an Image Quality Prior (IQP)-extracted from pre-trained No-Reference Image Quality Assessment (NR-IQA) models-to guide the restoration process toward perceptually optimal outputs explicitly. Our approach synergistically integrates IQP with a learned codebook prior through three key mechanisms: (1) a quality-conditioned Transformer, where NR-IQA-derived scores serve as conditioning signals to steer the predicted representation toward maximal perceptual quality. This design provides a plug-and-play enhancement compatible with existing restoration architectures without structural modification; and (2) a dual-branch codebook structure, which disentangles common and HQ-specific features, ensuring a comprehensive representation of both generic structural information and quality-sensitive attributes; and (3) a discrete representation-based quality optimization strategy, which mitigates over-optimization effects commonly observed in continuous latent spaces. Extensive experiments on real-world image restoration demonstrate that our method not only surpasses cutting-edge methods but also serves as a generalizable quality-guided enhancement strategy for existing methods. The code is available.
Abstract (translated)
URL
https://arxiv.org/abs/2603.29773
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Transmittance-Guided Structure-Texture Decomposition for Nighttime Image Dehazing
2026-03-31 09:47:58Francesco Moretti, Giulia Bianchi, Andrea GalloAbstract
Nighttime images captured under hazy conditions suffer from severe quality degradation, including low visibility, color distortion, and reduced contrast, caused by the combined effects of atmospheric scattering, absorption by suspended particles, and non-uniform illumination from artificial light sources. While existing nighttime dehazing methods have achieved partial success, they typically address only a subset of these issues, such as glow suppression or brightness enhancement, without jointly tackling the full spectrum of degradation factors. In this paper, we propose a two-stage nighttime image dehazing framework that integrates transmittance correction with structure-texture layered optimization. In the first stage, we introduce a novel transmittance correction method that establishes boundary-constrained initial transmittance maps and subsequently applies region-adaptive compensation and normalization based on whether image regions correspond to light source areas. A quadratic Gaussian filtering scheme operating in the YUV color space is employed to estimate the spatially varying atmospheric light map. The corrected transmittance map and atmospheric light map are then used in conjunction with an improved nighttime imaging model to produce the initial dehazed image. In the second stage, we propose a STAR-YUV decomposition model that separates the dehazed image into structure and texture layers within the YUV color space. Gamma correction and MSRCR-based color restoration are applied to the structure layer for illumination compensation and color bias correction, while Laplacian-of-Gaussian filtering is applied to the texture layer for detail enhancement. A novel two-phase fusion strategy, comprising nonlinear Retinex-based fusion of the enhanced layers followed by linear blending with the initial dehazing result, yields the final output.
Abstract (translated)
URL
https://arxiv.org/abs/2603.29507
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A2BFR: Attribute-Aware Blind Face Restoration
2026-03-31 08:29:04Chenxin Zhu, Yushun Fang, Lu Liu, Shibo Yin, Xiaohong Liu, Xiaoyun Zhang, Qiang Hu, Guangtao ZhaiAbstract
Blind face restoration (BFR) aims to recover high-quality facial images from degraded inputs, yet its inherently ill-posed nature leads to ambiguous and uncontrollable solutions. Recent diffusion-based BFR methods improve perceptual quality but remain uncontrollable, whereas text-guided face editing enables attribute manipulation without reliable restoration. To address these issues, we propose A$^2$BFR, an attribute-aware blind face restoration framework that unifies high-fidelity reconstruction with prompt-controllable generation. Built upon a Diffusion Transformer backbone with unified image-text cross-modal attention, A$^2$BFR jointly conditions the denoising trajectory on both degraded inputs and textual prompts. To inject semantic priors, we introduce attribute-aware learning, which supervises denoising latents using facial attribute embeddings extracted by an attribute-aware encoder. To further enhance prompt controllability, we introduce semantic dual-training, which leverages the pairwise attribute variations in our newly curated AttrFace-90K dataset to enforce attribute discrimination while preserving fidelity. Extensive experiments demonstrate that A$^2$BFR achieves state-of-the-art performance in both restoration fidelity and instruction adherence, outperforming diffusion-based BFR baselines by -0.0467 LPIPS and +52.58% attribute accuracy, while enabling fine-grained, prompt-controllable restoration even under severe degradations.
Abstract (translated)
盲人脸修复（BFR）旨在从退化输入中恢复高质量人脸图像，但其固有的病态性会导致模糊且不可控的结果。近期基于扩散模型的BFR方法提升了感知质量，但仍缺乏可控性；而文本引导的人脸编辑虽能实现属性操控，却难以保证可靠的修复效果。针对这些问题，我们提出了A²BFR——一种属性感知的盲人脸修复框架，实现了高保真重建与提示可控生成的统一。该框架基于扩散Transformer主干网络，通过统一的图像-文本跨模态注意力机制，使去噪轨迹同时依赖于退化输入与文本提示。为注入语义先验，我们引入属性感知学习，利用属性感知编码器提取的人脸属性嵌入来监督去噪隐空间。为进一步提升提示可控性，我们提出语义双重训练策略，利用新构建的AttrFace-90K数据集中的成对属性变化，在保持保真度的同时强化属性判别能力。大量实验表明，A²BFR在修复保真度与指令遵循性上均达到最优性能，相比基于扩散的BFR基线方法，LPIPS指标降低0.0467，属性准确率提升52.58%，并能在严重退化条件下实现细粒度、提示可控的修复。
URL
https://arxiv.org/abs/2603.29423
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Divide and Restore: A Modular Task-Decoupled Framework for Universal Image Restoration
2026-03-30 16:45:16Joanna Wiekiera, Martyna ZurAbstract
Restoring images affected by various types of degradation, such as noise, blur, or improper exposure, remains a significant challenge in computer vision. While recent trends favor complex monolithic all-in-one architectures, these models often suffer from negative task interference and require extensive joint training cycles on high-end computing clusters. In this paper, we propose a modular, task-decoupled image restoration framework based on an explicit diagnostic routing mechanism. The architecture consists of a lightweight Convolutional Neural Network (CNN) classifier that evaluates the input image and dynamically directs it to a specialized restoration node. A key advantage of this framework is its model-agnostic extensibility: while we demonstrate it using three independent U-Net experts, the system allows for the integration of any restoration method tailored to specific tasks. By isolating reconstruction paths, the framework prevents feature conflicts and significantly reduces training overhead. Unlike monolithic models, adding new degradation types in our framework only requires training a single expert and updating the router, rather than a full system retraining. Experimental results demonstrate that this computationally accessible approach offers a scalable and efficient solution for multi-degradation restoration on standard local hardware. The code will be published upon paper acceptance.
Abstract (translated)
URL
https://arxiv.org/abs/2603.28658
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Hydra: Unifying Document Retrieval and Generation in a Single Vision-Language Model
2026-03-30 15:17:41Athos GeorgiouAbstract
Visual document understanding typically requires separate retrieval and generation models, doubling memory and system complexity. We present Hydra, a dual-head approach that provides both ColBERT-style late-interaction retrieval and autoregressive generation from a single vision-language model (VLM). A single LoRA adapter, trained only for retrieval, is toggled at inference: enabling it produces multi-vector embeddings; disabling it recovers the base model's generation quality -- byte-identical outputs in 100% of 10,500 greedy and stochastic samples, with max delta-ANLS = 0.0044 across 15,301 samples on four VQA benchmarks (three informative; ChartQA is near-zero for both models under greedy decoding) when compared against an independent base-model pipeline. We identify three engineering requirements (attention-mode restoration, lm_head preservation, KV-cache-aware decoding) whose omission silently breaks generation despite correct weight recovery. On ViDoRe V1, Hydra (4B) is within 1 percentage point of a controlled single-head baseline in a single training run, with higher aggregate scores on V2 and V3 that are concentrated on a subset of tasks; multi-seed experiments are needed to confirm these trends. The single-model design reduces peak GPU memory by 41%, though adapter switching introduces throughput overhead under concurrent serving loads. An ablation shows that GritLM-style joint training provides no benefit within the LoRA-based (r=16) training regime. A proof-of-concept extension to Qwen2.5-Omni-3B demonstrates that the mechanism generalizes to audio retrieval and video embedding, with speech generation.
Abstract (translated)
URL
https://arxiv.org/abs/2603.28554
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Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation
2026-03-30 13:26:05Weichao Cai, Weiliang Huang, Biao Xue, Chao Huang, Fei Yuan, Bob ZhangAbstract
Marine scene understanding and segmentation plays a vital role in maritime monitoring and navigation safety. However, prevalent factors like fog and strong reflections in maritime environments cause severe image degradation, significantly compromising the stability of semantic perception. Existing restoration and enhancement methods typically target specific degradations or focus solely on visual quality, lacking end-to-end collaborative mechanisms that simultaneously improve structural recovery and semantic effectiveness. Moreover, publicly available infrared-visible datasets are predominantly collected from urban scenes, failing to capture the authentic characteristics of coupled degradations in marine environments. To address these challenges, the Infrared-Visible Maritime Ship Dataset (IVMSD) is proposed to cover various maritime scenarios under diverse weather and illumination conditions. Building upon this dataset, a Multi-task Complementary Learning Framework (MCLF) is proposed to collaboratively perform image restoration, multimodal fusion, and semantic segmentation within a unified architecture. The framework includes a Frequency-Spatial Enhancement Complementary (FSEC) module for degradation suppression and structural enhancement, a Semantic-Visual Consistency Attention (SVCA) module for semantic-consistent guidance, and a cross-modality guided attention mechanism for selective fusion. Experimental results on IVMSD demonstrate that the proposed method achieves state-of-the-art segmentation performance, significantly enhancing robustness and perceptual quality under complex maritime conditions.
Abstract (translated)
海洋场景理解与分割在海上监测和航行安全中起着至关重要的作用。然而，海上环境中常见的雾和强反射等因素会导致图像严重退化，显著影响语义感知的稳定性。现有的复原与增强方法通常针对特定退化类型，或仅关注视觉质量，缺乏能同时提升结构恢复与语义效果端到端的协同机制。此外，公开的红外-可见光数据集多采集自城市场景，未能真实反映海洋环境中耦合退化的特性。为应对这些挑战，本文提出红外-可见光海洋船舶数据集（IVMSD），涵盖不同天气与光照条件下的多种海上场景。基于该数据集，进一步提出多任务互补学习框架（MCLF），在统一架构中协同完成图像复原、多模态融合与语义分割任务。该框架包含用于退化抑制与结构增强的频空增强互补模块（FSEC）、用于语义一致引导的语义视觉一致性注意力模块（SVCA），以及用于选择性融合的跨模态引导注意力机制。在IVMSD上的实验结果表明，所提方法实现了最先进的分割性能，显著提升了复杂海况下的鲁棒性与感知质量。
URL
https://arxiv.org/abs/2603.28414
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Prints in the Magnetic Dust: Robust Similarity Search in Legacy Media Images Using Checksum Count Vectors
2026-03-30 12:01:59Maciej Grzeszczuk, Kinga Skorupska, Grzegorz M. W\'ojcikAbstract
Digitizing magnetic media containing computer data is only the first step towards the preservation of early home computing era artifacts. The audio tape images must be decoded, verified, repaired if necessary, tested, and documented. If parts of this process could be effectively automated, volunteers could focus on contributing contextual and historical knowledge rather than struggling with technical tools. We therefore propose a feature representation based on Checksum Count Vectors and evaluate its applicability to detecting duplicates and variants of recordings within a large data store. The approach was tested on a collection of decoded tape images (n=4902), achieving 58\% accuracy in detecting variants and 97% accuracy in identifying alternative copies, for damaged recordings with up to 75% of records missing. These results represent an important step towards fully automated pipelines for restoration, de-duplication, and semantic integration of historical digital artifacts through sequence matching, automatic repair and knowledge discovery.
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URL
https://arxiv.org/abs/2604.09657
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ColorFLUX: A Structure-Color Decoupling Framework for Old Photo Colorization
2026-03-30 08:29:16Bingchen Li, Zhixin Wang, Fan Li, Jiaqi Xu, Jiaming Guo, Renjing Pei, Xin Li, Zhibo ChenAbstract
Old photos preserve invaluable historical memories, making their restoration and colorization highly desirable. While existing restoration models can address some degradation issues like denoising and scratch removal, they often struggle with accurate colorization. This limitation arises from the unique degradation inherent in old photos, such as faded brightness and altered color hues, which are different from modern photo distributions, creating a substantial domain gap during colorization. In this paper, we propose a novel old photo colorization framework based on the generative diffusion model FLUX. Our approach introduces a structure-color decoupling strategy that separates structure preservation from color restoration, enabling accurate colorization of old photos while maintaining structural consistency. We further enhance the model with a progressive Direct Preference Optimization (Pro-DPO) strategy, which allows the model to learn subtle color preferences through coarse-to-fine transitions in color augmentation. Additionally, we address the limitations of text-based prompts by introducing visual semantic prompts, which extract fine-grained semantic information directly from old photos, helping to eliminate the color bias inherent in old photos. Experimental results on both synthetic and real datasets demonstrate that our approach outperforms existing state-of-the-art colorization methods, including closed-source commercial models, producing high-quality and vivid colorization.
Abstract (translated)
URL
https://arxiv.org/abs/2603.28162
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RetinexDualV2: Physically-Grounded Dual Retinex for Generalized UHD Image Restoration
2026-03-30 03:00:32Mohab Kishawy, Jun ChenAbstract
We propose RetinexDualV2, a unified, physically grounded dual-branch framework for diverse Ultra-High-Definition (UHD) image restoration. Unlike generic models, our method employs a Task-Specific Physical Grounding Module (TS-PGM) to extract degradation-aware priors (e.g., rain masks and dark channels). These explicitly guide a Retinex decomposition network via a novel Physical-conditioned Multi-head Self-Attention (PC-MSA) mechanism, enabling robust reflection and illumination correction. This physical conditioning allows a single architecture to handle various complex degradations seamlessly, without task-specific structural modifications. RetinexDualV2 demonstrates exceptional generalizability, securing 4\textsuperscript{th} place in the NTIRE 2026 Day and Night Raindrop Removal Challenge and 5\textsuperscript{th} place in the Joint Noise Low-light Enhancement (JNLLIE) Challenge. Extensive experiments confirm the state-of-the-art performance and efficiency of our physically motivated approach.
Abstract (translated)
我们提出了RetinexDualV2，一个统一的、基于物理原理的双分支框架，用于处理多样化的超高清（UHD）图像恢复任务。与通用模型不同，我们的方法采用任务特定物理引导模块（TS-PGM）来提取退化感知先验（例如雨掩膜和暗通道）。这些先验通过一种新颖的物理条件多头自注意力（PC-MSA）机制，明确指导Retinex分解网络，从而实现鲁棒的反射和光照校正。这种物理条件化使得单一架构能够无缝处理各种复杂退化问题，而无需针对特定任务进行结构修改。RetinexDualV2展现出卓越的泛化能力，在NTIRE 2026昼夜雨滴去除挑战赛中位列第四，在联合噪声低光增强（JNLLIE）挑战赛中排名第五。大量实验验证了我们这种基于物理动机的方法具有卓越的性能和效率。
URL
https://arxiv.org/abs/2603.27979
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TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration
2026-03-29 15:50:36Yisheng Zhang, Guoli Jia, Haote Hu, Shanxu Zhao, Kaikai Zhao, Long Sun, Xinwei Long, Kai Tian, Che Jiang, Zhaoxiang Liu, Kai Wang, Shiguo Lian, Kaiyan Zhang, Bowen ZhouAbstract
Vision-language agents that orchestrate specialized tools for image restoration (IR) have emerged as a promising method, yet most existing frameworks operate in a training-free manner. They rely on heuristic task scheduling and exhaustive tool traversal, resulting in sub-optimal restoration paths and prohibitive computational cost. We argue that the core bottleneck lies in the absence of a learned policy to make decision, as a vision-language model cannot efficiently handle degradation-aware task ordering and tool composition. To this end, we propose TIR-Agent, a trainable image restoration agent that performs a direct tool-calling policy through a two-stage training pipeline of supervised fine-tuning (SFT) followed by reinforcement learning (RL). Two key designs underpin effective RL training: (i) a random perturbation strategy applied to the SFT data, which broadens the policy's exploration over task schedules and tool compositions, and (ii) a multi-dimensional adaptive reward mechanism that dynamically re-weights heterogeneous image quality metrics to mitigate reward hacking. To support high-throughput, asynchronous GPU-based tool invocation during training, we further develop a globally shared model-call pool. Experiments on both in-domain and out-of-domain degradations show that TIR-Agent outperforms 12 baselines, including 6 all-in-one models, 3 training-free agents, and 3 proprietary models, and achieves over 2.5$\times$ inference speedup by eliminating redundant tool executions.
Abstract (translated)
URL
https://arxiv.org/abs/2603.27742
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The Degree of Language Diacriticity and Its Effect on Tasks
Abstract
Diacritics are orthographic marks that clarify pronunciation, distinguish similar words, or alter meaning. They play a central role in many writing systems, yet their impact on language technology has not been systematically quantified across scripts. While prior work has examined diacritics in individual languages, there's no cross-linguistic, data-driven framework for measuring the degree to which writing systems rely on them and how this affects downstream tasks. We propose a data-driven framework for quantifying diacritic complexity using corpus-level, information-theoretic metrics that capture the frequency, ambiguity, and structural diversity of character-diacritic combinations. We compute these metrics over 24 corpora in 15 languages, spanning both single- and multi-diacritic scripts. We then examine how diacritic complexity correlates with performance on the task of diacritics restoration, evaluating BERT- and RNN-based models. We find that across languages, higher diacritic complexity is strongly associated with lower restoration accuracy. In single-diacritic scripts, where character-diacritic combinations are more predictable, frequency-based and structural measures largely align. In multi-diacritic scripts, however, structural complexity exhibits the strongest association with performance, surpassing frequency-based measures. These findings show that measurable properties of diacritic usage influence the performance of diacritic restoration models, demonstrating that orthographic complexity is not only descriptive but functionally relevant for modeling.
Abstract (translated)
URL
https://arxiv.org/abs/2603.27653
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A Robust Low-Rank Prior Model for Structured Cartoon-Texture Image Decomposition with Heavy-Tailed Noise
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Cartoon-texture image decomposition is a fundamental yet challenging problem in image processing. A significant hurdle in achieving accurate decomposition is the pervasive presence of noise in the observed images, which severely impedes robust results. To address the challenging problem of cartoon-texture decomposition in the presence of heavy-tailed noise, we in this paper propose a robust low-rank prior model. Our approach departs from conventional models by adopting the Huber loss function as the data-fidelity term, rather than the traditional $\ell_2$-norm, while retaining the total variation norm and nuclear norm to characterize the cartoon and texture components, respectively. Given the inherent structure, we employ two implementable operator splitting algorithms, tailored to different degradation operators. Extensive numerical experiments, particularly on image restoration tasks under high-intensity heavy-tailed noise, efficiently demonstrate the superior performance of our model.
Abstract (translated)
卡通-纹理分解是图像处理中一个基础且具有挑战性的问题。在实现精确分解时，一个主要障碍是观测图像中普遍存在的噪声，这会严重阻碍获得稳健的结果。为应对重尾噪声环境下卡通-纹理分解的难题，本文提出一种鲁棒低秩先验模型。该模型区别于传统方法：采用Huber损失函数作为数据保真项以替代传统的$\ell_2$范数，同时保留总变分范数和核范数分别刻画卡通成分与纹理成分。基于模型的内在结构，我们针对不同的退化算子设计了两类可实现的算子分裂算法。大量数值实验，特别是针对高强度重尾噪声下的图像复原任务，高效验证了本模型优越的性能。
URL
https://arxiv.org/abs/2603.27579
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OmniColor: A Unified Framework for Multi-modal Lineart Colorization
2026-03-29 05:46:18Xulu Zhang, Haoqian Du, Xiaoyong Wei, Qing LiAbstract
Lineart colorization is a critical stage in professional content creation, yet achieving precise and flexible results under diverse user constraints remains a significant challenge. To address this, we propose OmniColor, a unified framework for multi-modal lineart colorization that supports arbitrary combinations of control signals. Specifically, we systematically categorize guidance signals into two types: spatially-aligned conditions and semantic-reference conditions. For spatially-aligned inputs, we employ a dual-path encoding strategy paired with a Dense Feature Alignment loss to ensure rigorous boundary preservation and precise color restoration. For semantic-reference inputs, we utilize a VLM-only encoding scheme integrated with a Temporal Redundancy Elimination mechanism to filter repetitive information and enhance inference efficiency. To resolve potential input conflicts, we introduce an Adaptive Spatial-Semantic Gating module that dynamically balances multi-modal constraints. Experimental results demonstrate that OmniColor achieves superior controllability, visual quality, and temporal stability, providing a robust and practical solution for lineart colorization. The source code and dataset will be open at this https URL.
Abstract (translated)
线稿上色是专业内容创作中的关键环节，但在多样化的用户约束条件下实现精确且灵活的结果仍是一项重大挑战。为此，我们提出了OmniColor，一个支持任意控制信号组合的统一多模态线稿上色框架。具体而言，我们系统地将引导信号分为两类：空间对齐条件与语义参考条件。对于空间对齐输入，我们采用双路径编码策略并结合密集特征对齐损失，以确保严格的边界保持与精确的色彩还原。对于语义参考输入，我们使用纯视觉语言模型编码方案，并集成时间冗余消除机制以过滤重复信息、提升推理效率。为解决潜在的输入冲突，我们引入了自适应空间-语义门控模块，以动态平衡多模态约束。实验结果表明，OmniColor在可控性、视觉质量与时序稳定性上均达到优异表现，为线稿上色提供了稳健实用的解决方案。源代码与数据集将在以下链接公开：https://URL。
URL
https://arxiv.org/abs/2603.27531
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From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion
2026-03-27 16:51:40D\'avid Pukanec, Tibor Kub\'ik, Michal \v{S}pan\v{e}lAbstract
We present ToothCraft, a diffusion-based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth. Building upon recent advancements in conditioned diffusion models for 3D shapes, we developed a model capable of an automated tooth crown completion conditioned on local anatomical context. To address the lack of training data for this task, we designed an augmentation pipeline that generates incomplete tooth geometries from a publicly available dataset of complete dental arches (3DS, ODD). By synthesising a diverse set of training examples, our approach enables robust learning across a wide spectrum of tooth defects. Experimental results demonstrate the strong capability of our model to reconstruct complete tooth crowns, achieving an intersection over union (IoU) of 81.8% and a Chamfer Distance (CD) of 0.00034 on synthetically damaged testing restorations. Our experiments demonstrate that the model can be applied directly to real-world cases, effectively filling in incomplete teeth, while generated crowns show minimal intersection with the opposing dentition, thus reducing the risk of occlusal interference. Access to the code, model weights, and dataset information will be available at: this https URL
Abstract (translated)
我们提出了ToothCraft，这是一种基于扩散模型的上下文感知牙冠生成方法，该模型在人工生成的不完整牙齿数据上进行了训练。基于近期条件扩散模型在3D形状领域的进展，我们开发了一种能够根据局部解剖学上下文自动完成牙冠修复的模型。针对该任务训练数据匮乏的问题，我们设计了一个增强流程，可从公开的完整牙弓数据集（3DS、ODD）中生成不完整的牙齿几何体。通过合成多样化的训练样本，我们的方法能够对广泛类型的牙齿缺陷进行鲁棒学习。实验结果表明，该模型在重建完整牙冠方面表现出色，在合成损伤测试修复体上实现了81.8%的交并比（IoU）和0.00034的倒角距离（CD）。我们的实验证明该模型可直接应用于实际病例，有效填补不完整牙齿，同时生成的牙冠与对颌牙列交集极小，从而降低了咬合干扰风险。代码、模型权重及数据集信息详见：此https链接
URL
https://arxiv.org/abs/2603.26588
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Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration
2026-03-27 13:13:44I-Hsiang Chen, Isma Hadji, Enrique Sanchez, Adrian Bulat, Sy-Yen Kuo, Radu Timofte, Georgios Tzimiropoulos, Brais MartinezAbstract
Image restoration aims to recover high quality images from inputs degraded by various factors, such as adverse weather, blur, or low light. While recent studies have shown remarkable progress across individual or unified restoration tasks, they still suffer from limited generalization and inefficiency when handling unknown or composite degradations. To address these limitations, we propose RAR, a Restore, Assess and Repeat process, that integrates Image Quality Assessment (IQA) and Image Restoration (IR) into a unified framework to iteratively and efficiently achieve high quality image restoration. Specifically, we introduce a restoration process that operates entirely in the latent domain to jointly perform degradation identification, image restoration, and quality verification. The resulting model is fully trainable end to end and allows for an all-in-one assess and restore approach that dynamically adapts the restoration process. Also, the tight integration of IQA and IR into a unified model minimizes the latency and information loss that typically arises from keeping the two modules disjoint, (e.g. during image and/or text decoding). Extensive experiments show that our approach consistent improvements under single, unknown and composite degradations, thereby establishing a new state-of-the-art.
Abstract (translated)
图像恢复旨在从受恶劣天气、模糊或低光照等多种因素退化的输入中重建高质量图像。尽管近期研究在单一或统一恢复任务上取得了显著进展，但在处理未知或复合退化时仍存在泛化能力有限和效率低下的问题。为克服这些局限，我们提出RAR（Restore, Assess and Repeat）流程，将图像质量评估（IQA）与图像恢复（IR）集成到统一框架中，通过迭代方式高效实现高质量图像恢复。具体而言，我们引入完全在潜在域运行的恢复流程，协同完成退化识别、图像恢复与质量验证。所得模型支持端到端全流程训练，实现动态适应恢复过程的“一体化评估-恢复”方案。同时，IQA与IR的紧密集成最小了传统分离模块（如图像/文本解码阶段）带来的延迟与信息损失。大量实验表明，该方法在单一、未知及复合退化场景下均持续提升性能，确立了新的最先进水平。
URL
https://arxiv.org/abs/2603.26385
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Provably Contractive and High-Quality Denoisers for Convergent Restoration
2026-03-27 08:35:55Shubhi Shukla, Pravin NairAbstract
Image restoration, the recovery of clean images from degraded measurements, has applications in various domains like surveillance, defense, and medical imaging. Despite achieving state-of-the-art (SOTA) restoration performance, existing convolutional and attention-based networks lack stability guarantees under minor shifts in input, exposing a robustness accuracy trade-off. We develop provably contractive (global Lipschitz $< 1$) denoiser networks that considerably reduce this gap. Our design composes proximal layers obtained from unfolding techniques, with Lipschitz-controlled convolutional refinements. By contractivity, our denoiser guarantees that input perturbations of strength $\|\delta\|\le\varepsilon$ induce at most $\varepsilon$ change at the output, while strong baselines such as DnCNN and Restormer can exhibit larger deviations under the same perturbations. On image denoising, the proposed model is competitive with unconstrained SOTA denoisers, reporting the tightest gap for a provably 1-Lipschitz model and establishing that such gaps are indeed achievable by contractive denoisers. Moreover, the proposed denoisers act as strong regularizers for image restoration that provably effect convergence in Plug-and-Play algorithms. Our results show that enforcing strict Lipschitz control does not inherently degrade output quality, challenging a common assumption in the literature and moving the field toward verifiable and stable vision models. Codes and pretrained models are available at this https URL
Abstract (translated)
图像恢复旨在从退化观测中重建清晰图像，在监控、国防和医学成像等领域具有广泛应用。尽管现有基于卷积和注意力的网络已实现最先进的恢复性能，但它们在输入发生微小偏移时缺乏稳定性保证，暴露出鲁棒性与精度之间的权衡。我们开发了可证明具有压缩性（全局Lipschitz常数<1）的去噪网络，显著缩小了这一差距。我们的设计通过展开技术获得近端层，并结合Lipschitz约束的卷积精修进行组合。基于压缩性，我们的去噪器保证：当输入扰动强度满足$\|\delta\|\le\varepsilon$时，输出变化不超过$\varepsilon$；而DnCNN和Restormer等强基线模型在相同扰动下可能产生更大偏差。在图像去噪任务中，所提模型与无约束的最先进去噪器具有竞争力，为可证明1-Lipschitz模型实现了最紧凑的精度差距，证实了压缩性去噪器确实能够达到此类性能。此外，提出的去噪器作为图像恢复的强正则器，可证明能促进即插即用算法的收敛。我们的研究表明，施加严格的Lipschitz约束并不会固有降低输出质量，这挑战了文献中的常见假设，推动领域向可验证、稳定的视觉模型发展。代码与预训练模型见以下网址：https URL
URL
https://arxiv.org/abs/2603.26168
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RealRestorer: Towards Generalizable Real-World Image Restoration with Large-Scale Image Editing Models
2026-03-26 14:39:39Yufeng Yang, Xianfang Zeng, Zhangqi Jiang, Fukun Yin, Jianzhuang Liu, Wei Cheng, jinghong lan, Shiyu Liu, Yuqi Peng, Gang YU, Shifeng ChenAbstract
Image restoration under real-world degradations is critical for downstream tasks such as autonomous driving and object detection. However, existing restoration models are often limited by the scale and distribution of their training data, resulting in poor generalization to real-world scenarios. Recently, large-scale image editing models have shown strong generalization ability in restoration tasks, especially for closed-source models like Nano Banana Pro, which can restore images while preserving consistency. Nevertheless, achieving such performance with those large universal models requires substantial data and computational costs. To address this issue, we construct a large-scale dataset covering nine common real-world degradation types and train a state-of-the-art open-source model to narrow the gap with closed-source alternatives. Furthermore, we introduce RealIR-Bench, which contains 464 real-world degraded images and tailored evaluation metrics focusing on degradation removal and consistency preservation. Extensive experiments demonstrate our model ranks first among open-source methods, achieving state-of-the-art performance.
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URL
https://arxiv.org/abs/2603.25502
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Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework
2026-03-26 10:40:08Hongru Han, Tingrui Guo, Liming Zhang, Yan Su, Qiwen Xu, Zhuohua YeAbstract
Low-light image enhancement (LLIE) has traditionally been formulated as a deterministic mapping. However, this paradigm often struggles to account for the ill-posed nature of the task, where unknown ambient conditions and sensor parameters create a multimodal solution space. Consequently, state-of-the-art methods frequently encounter luminance discrepancies between predictions and labels, often necessitating "gt-mean" post-processing to align output luminance for evaluation. To address this fundamental limitation, we propose a transition toward Controllable Low-light Enhancement (CLE), explicitly reformulating the task as a well-posed conditional problem. To this end, we introduce CLE-RWKV, a holistic framework supported by Light100, a new benchmark featuring continuous real-world illumination transitions. To resolve the conflict between luminance control and chromatic fidelity, a noise-decoupled supervision strategy in the HVI color space is employed, effectively separating illumination modulation from texture restoration. Architecturally, to adapt efficient State Space Models (SSMs) for dense prediction, we leverage a Space-to-Depth (S2D) strategy. By folding spatial neighborhoods into channel dimensions, this design allows the model to recover local inductive biases and effectively bridge the "scanning gap" inherent in flattened visual sequences without sacrificing linear complexity. Experiments across seven benchmarks demonstrate that our approach achieves competitive performance and robust controllability, providing a real-world multi-illumination alternative that significantly reduces the reliance on gt-mean post-processing.
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URL
https://arxiv.org/abs/2603.25296
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Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Models
2026-03-26 08:18:10Chengyu Fang, Heng Guo, Zheng Jiang, Chunming He, Xiu Li, Minfeng XuAbstract
Multimodal large language models are promising for clinical visual question answering tasks, but scaling to 3D imaging is hindered by high computational costs. Prior methods often rely on 2D slices or fixed-length token compression, disrupting volumetric continuity and obscuring subtle findings. We present Photon, a framework that represents 3D medical volumes with token sequences of variable length. Photon introduces instruction-conditioned token scheduling and surrogate gradient propagation to adaptively reduce tokens during both training and inference, which lowers computational cost while mitigating the attention dilution caused by redundant tokens. It incorporates a custom backpropagation rule with gradient restoration to enable differentiable optimization despite discrete token drop. To stabilize token compression and ensure reliable use of visual evidence, Photon further applies regularization objectives that mitigate language-only bias and improve reliability. Experiments on diverse medical visual question answering tasks show that Photon achieves state-of-the-art accuracy while reducing resource usage and accelerating both training and inference.
Abstract (translated)
多模态大语言模型在临床视觉问答任务中前景广阔，但扩展到3D影像时受限于高昂的计算成本。现有方法常依赖2D切片或固定长度的令牌压缩，破坏了体积连续性并掩盖了细微病灶。我们提出Photon框架，该框架以可变长度的令牌序列表示3D医学影像。Photon引入了指令条件化令牌调度与替代梯度传播机制，可在训练和推理阶段自适应减少令牌数量，从而在降低计算成本的同时缓解冗余令牌导致的注意力稀释问题。其采用自定义反向传播规则与梯度恢复技术，使离散令牌丢弃过程仍可实现可微优化。为稳定令牌压缩并确保视觉证据的可靠利用，Photon进一步应用正则化目标来减轻纯语言偏差并提升可靠性。在多种医学视觉问答任务上的实验表明，Photon在减少资源消耗、加速训练与推理的同时，实现了最先进的准确率。
URL
https://arxiv.org/abs/2603.25155
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