Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation

• URL: http://arxiv.org/abs/2511.04920v1
• Date: Fri, 07 Nov 2025 01:50:36 GMT
• Title: Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation
• Authors: Hu Gao, Xiaoning Lei, Ying Zhang, Xichen Xu, Guannan Jiang, Lizhuang Ma,
• Abstract summary: 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.
• Score: 51.10017611491389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration (IR) aims to recover clean images from degraded observations. Despite remarkable progress, most existing methods focus on a single degradation type, whereas real-world images often suffer from multiple coexisting degradations, such as rain, noise, and haze coexisting in a single image, which limits their practical effectiveness. In this paper, we propose an adaptive multi-degradation image restoration network that reconstructs images by leveraging decoupled representations of degradation ingredients to guide path selection. Specifically, we design a degradation ingredient decoupling block (DIDBlock) in the encoder to separate degradation ingredients statistically by integrating spatial and frequency domain information, enhancing the recognition of multiple degradation types and making their feature representations independent. In addition, we present fusion block (FBlock) to integrate degradation information across all levels using learnable matrices. In the decoder, we further introduce a task adaptation block (TABlock) that dynamically activates or fuses functional branches based on the multi-degradation representation, flexibly selecting optimal restoration paths under diverse degradation conditions. The resulting tightly integrated architecture, termed IMDNet, is extensively validated through experiments, showing superior performance on multi-degradation restoration while maintaining strong competitiveness on single-degradation tasks.

Related papers

• 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)
• UniRes: Universal Image Restoration for Complex Degradations [53.74404005987783]
  Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines.<n>A simple yet flexible diffusionbased framework, named UniRes, is proposed to address such degradations in an end-to-end manner.<n>Our proposed method is evaluated on both complex-degradation and single-degradation image restoration datasets.
  arXiv  Detail & Related papers  (2025-06-05T21:25:39Z)
• 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)
• 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)
• UniUIR: Considering Underwater Image Restoration as An All-in-One Learner [62.65503609562905]
  We propose a Universal Underwater Image Restoration method, termed as UniUIR.<n>To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module.<n>This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts.
  arXiv  Detail & Related papers  (2025-01-22T16:10:42Z)
• Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding [67.57487747508179]
  Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degraded image restoration with a single model. In this paper, we propose a novel multiple-in-one IR model that can effectively restore images with both single and mixed degradations.
  arXiv  Detail & Related papers  (2024-11-25T09:26:34Z)
• Prompt-based Ingredient-Oriented All-in-One Image Restoration [0.0]
  We propose a novel data ingredient-oriented approach to tackle multiple image degradation tasks. Specifically, we utilize a encoder to capture features and introduce prompts with degradation-specific information to guide the decoder. Our method performs competitively to the state-of-the-art.
  arXiv  Detail & Related papers  (2023-09-06T15:05:04Z)
• All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation [47.00239809958627]
  We propose a novel All-in-one Multi-degradation Image Restoration Network (AMIRNet) AMIRNet learns a degradation representation for unknown degraded images by progressively constructing a tree structure through clustering. This tree-structured representation explicitly reflects the consistency and discrepancy of various distortions, providing a specific clue for image restoration.
  arXiv  Detail & Related papers  (2023-08-06T04:51:41Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.