Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration

• URL: http://arxiv.org/abs/2504.09973v1
• Date: Mon, 14 Apr 2025 08:24:57 GMT
• Title: Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration
• Authors: Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, Liqiang Nie,
• Abstract summary: Contrastive Prompt Learning (CPL) is a novel framework that fundamentally enhances prompt-task alignment.<n>Our framework establishes new state-of-the-art performance while maintaining parameter efficiency, offering a principled solution for unified image restoration.
• Score: 109.38288333994407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: All-in-one image restoration, addressing diverse degradation types with a unified model, presents significant challenges in designing task-specific prompts that effectively guide restoration across multiple degradation scenarios. While adaptive prompt learning enables end-to-end optimization, it often yields overlapping or redundant task representations. Conversely, explicit prompts derived from pretrained classifiers enhance discriminability but may discard critical visual information for reconstruction. To address these limitations, we introduce Contrastive Prompt Learning (CPL), a novel framework that fundamentally enhances prompt-task alignment through two complementary innovations: a \emph{Sparse Prompt Module (SPM)} that efficiently captures degradation-specific features while minimizing redundancy, and a \emph{Contrastive Prompt Regularization (CPR)} that explicitly strengthens task boundaries by incorporating negative prompt samples across different degradation types. Unlike previous approaches that focus primarily on degradation classification, CPL optimizes the critical interaction between prompts and the restoration model itself. Extensive experiments across five comprehensive benchmarks demonstrate that CPL consistently enhances state-of-the-art all-in-one restoration models, achieving significant improvements in both standard multi-task scenarios and challenging composite degradation settings. Our framework establishes new state-of-the-art performance while maintaining parameter efficiency, offering a principled solution for unified image restoration.

Related papers

• 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)
• PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation [28.105125164852367]
  We propose PromptHSI, the first universal AiO HSI restoration framework.<n>Our approach decomposes text prompts into intensity and bias controllers that effectively guide the restoration process.<n>Our architecture excels at both fine-grained recovery and global information restoration across diverse degradation scenarios.
  arXiv  Detail & Related papers  (2024-11-24T17:08:58Z)
• Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration [33.163161549726446]
  Perceive-IR is an all-in-one image restorer designed to achieve fine-grained quality control. In the prompt learning stage, we leverage prompt learning to acquire a fine-grained quality perceiver capable of distinguishing three-tier quality levels. For the restoration stage, a semantic guidance module and compact feature extraction are proposed to further promote the restoration process.
  arXiv  Detail & Related papers  (2024-08-28T17:58:54Z)
• Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution [33.16889233975723]
  Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community. We propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework.
  arXiv  Detail & Related papers  (2024-08-10T04:51:43Z)
• Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance [12.066756224383827]
  textbfRestorer is a novel Transformer-based all-in-one image restoration model. It can handle composite degradation in real-world scenarios without requiring additional training. It is efficient during inference, suggesting the potential in real-world applications.
  arXiv  Detail & Related papers  (2024-06-18T13:18:32Z)
• SPIRE: Semantic Prompt-Driven Image Restoration [66.26165625929747]
  We develop SPIRE, a Semantic and restoration Prompt-driven Image Restoration framework. Our approach is the first framework that supports fine-level instruction through language-based quantitative specification of the restoration strength. Our experiments demonstrate the superior restoration performance of SPIRE compared to the state of the arts.
  arXiv  Detail & Related papers  (2023-12-18T17:02:30Z)
• Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration [78.14941737723501]
  We propose a Cross-consistent Deep Unfolding Network (CDUN) for All-In-One VR. By orchestrating two cascading procedures, CDUN achieves adaptive processing for diverse degradations. In addition, we introduce a window-based inter-frame fusion strategy to utilize information from more adjacent frames.
  arXiv  Detail & Related papers  (2023-09-04T14:18:00Z)
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting [112.66832145320434]
  We introduce a self-regularization framework for prompting called PromptSRC. PromptSRC guides the prompts to optimize for both task-specific and task-agnostic general representations.
  arXiv  Detail & Related papers  (2023-07-13T17:59:35Z)
• Implicit Subspace Prior Learning for Dual-Blind Face Restoration [66.67059961379923]
  A novel implicit subspace prior learning (ISPL) framework is proposed as a generic solution to dual-blind face restoration. Experimental results demonstrate significant perception-distortion improvement of ISPL against existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-12T08:04:24Z)

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.