Divide and Restore: A Modular Task-Decoupled Framework for Universal Image Restoration

• URL: http://arxiv.org/abs/2603.28658v1
• Date: Mon, 30 Mar 2026 16:45:16 GMT
• Title: Divide and Restore: A Modular Task-Decoupled Framework for Universal Image Restoration
• Abstract summary: Restoring images affected by various types of degradation remains a significant challenge in computer vision.<n>We propose a modular, task-decoupled image restoration framework based on an explicit diagnostic routing mechanism.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Universal Image Restoration via Internalized Chain-of-Thought Reasoning [52.763760468548504]
  Image restoration seeks to recover high-quality images from degraded inputs but becomes highly ill-posed under complex, mixed degradations.<n>Recent works adopt Chain-of-Thought (CoT) reasoning for multi-round restoration using specialized modules.<n>We introduce CoTIR, a universal image restoration framework that internalizes CoT reasoning within a single model.
  arXiv  Detail & Related papers  (2026-06-16T06:01:36Z)
• Task-Guided Prompting for Unified Remote Sensing Image Restoration [19.11145604183771]
  TGPNet is a unified framework capable of handling denoising, cloud removal, shadow removal, deblurring, and SAR despeckling.<n>The core of our framework is a novel Task-Guided Prompting (TGP) strategy.<n>This work presents a significant advancement for multi-task RSIR, offering a practical and scalable solution for operational pipelines.
  arXiv  Detail & Related papers  (2026-04-03T05:29:12Z)
• UnSCAR: Universal, Scalable, Controllable, and Adaptable Image Restoration [20.447922377156143]
  Universal image restoration aims to recover clean images from arbitrary real-world degradations.<n>Existing all-in-one restoration networks do not scale to multiple degradations.<n>We introduce a unified inference pipeline with a mixture-of-experts architecture that decomposes restoration knowledge across specialized task-adaptable experts.
  arXiv  Detail & Related papers  (2026-03-08T01:35:54Z)
• UniSER: A Foundation Model for Unified Soft Effects Removal [72.60782767314713]
  We introduce UniSER, capable of addressing diverse degradations caused by soft effects within a single framework.<n>Our methodology centers on curating a massive 3.8M-pair dataset to ensure robustness and generalization.<n>This synergistic approach allows UniSER to significantly outperform both specialist and generalist models.
  arXiv  Detail & Related papers  (2025-11-18T06:39:39Z)
• Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution [45.571542528079114]
  We propose a novel interpretability-driven approach for multi-degradation image restoration.<n>We employ an improved second-order semi-smooth Newton algorithm to ensure that each module maintains clear physical interpretability.<n>To further enhance interpretability and adaptability, we design an explainable convolution module inspired by the human brain's flexible information processing.
  arXiv  Detail & Related papers  (2025-11-13T10:27:41Z)
• Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation [51.10017611491389]
  Real-world images often suffer from multiple coexisting degradations, such as rain, noise, and haze coexisting in a single image.<n>We propose an adaptive multi-degradation image restoration network that reconstructs images by leveraging decoupled representations of degradation ingredients.<n>The resulting tightly integrated architecture, termed IMDNet, is extensively validated through experiments.
  arXiv  Detail & Related papers  (2025-11-07T01:50:36Z)
• MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration [62.929029990341796]
  Real-world videos often suffer from complex degradations, such as noise, compression artifacts, and low-light distortions.<n>We propose MoA-VR, which mimics the reasoning and processing procedures of human professionals through three coordinated agents.<n>Specifically, we construct a large-scale and high-resolution video degradation recognition benchmark and build a vision-language model (VLM) driven degradation identifier.
  arXiv  Detail & Related papers  (2025-10-09T17:42:51Z)
• Manifold-aware Representation Learning for Degradation-agnostic Image Restoration [135.90908995927194]
  Image Restoration (IR) aims to recover high quality images from degraded inputs affected by various corruptions such as noise, blur, haze, rain, and low light conditions.<n>We present MIRAGE, a unified framework for all in one IR that explicitly decomposes the input feature space into three semantically aligned parallel branches.<n>This modular decomposition significantly improves generalization and efficiency across diverse degradations.
  arXiv  Detail & Related papers  (2025-05-24T12:52:10Z)
• Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation [158.37640586809187]
  Restoring any degraded image efficiently via just one model has become increasingly significant.<n>Our approach, termed AnyIR, takes a unified path that leverages inherent similarity across various degradations.<n>To fuse the degradation awareness and the contextualized attention, a spatial-frequency parallel fusion strategy is proposed.
  arXiv  Detail & Related papers  (2025-04-19T09:54:46Z)
• InvFussion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems [76.39776789410088]
  This work introduces a framework that combines the strong performance of supervised approaches and the flexibility of zero-shot methods.<n>A novel architectural design seamlessly integrates the degradation operator directly into the denoiser.<n> Experimental results on the FFHQ and ImageNet datasets demonstrate state-of-the-art posterior-sampling performance.
  arXiv  Detail & Related papers  (2025-04-02T12:40:57Z)
• UniCoRN: Latent Diffusion-based Unified Controllable Image Restoration Network across Multiple Degradations [4.892790389883125]
  We propose UniCoRN, a unified image restoration approach capable of handling multiple degradation types simultaneously.<n>Specifically, we uncover the potential of low-level visual cues extracted from images in guiding a controllable diffusion model.<n>We also introduce MetaRestore, a metalens imaging benchmark containing images with multiple degradations and artifacts.
  arXiv  Detail & Related papers  (2025-03-20T05:42:13Z)
• Mixed Hierarchy Network for Image Restoration [0.0]
  We present a mixed hierarchy network that can balance quality and system complexity in image restoration. Our model first learns the contextual information using encoder-decoder architectures, and then combines them with high-resolution branches that preserve spatial detail. The resulting tightly interlinked hierarchy architecture, named as MHNet, delivers strong performance gains on several image restoration tasks.
  arXiv  Detail & Related papers  (2023-02-19T12:18:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.