URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration

• URL: http://arxiv.org/abs/2505.23068v1
• Date: Thu, 29 May 2025 04:17:09 GMT
• Title: URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration
• Authors: Rui Xu, Yuzhen Niu, Yuezhou Li, Huangbiao Xu, Wenxi Liu, Yuzhong Chen,
• Abstract summary: We introduce a Unified Receptance Weighted Key Value (URWKV) model with multi-state perspective.<n>We customize the core URWKV block to perceive and analyze complex degradations by leveraging multiple intra- and inter-stage states.<n>In comparison to state-of-the-art models, our URWKV model achieves superior performance on various benchmarks.
• Score: 22.746234919635018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing low-light image enhancement (LLIE) and joint LLIE and deblurring (LLIE-deblur) models have made strides in addressing predefined degradations, yet they are often constrained by dynamically coupled degradations. To address these challenges, we introduce a Unified Receptance Weighted Key Value (URWKV) model with multi-state perspective, enabling flexible and effective degradation restoration for low-light images. Specifically, we customize the core URWKV block to perceive and analyze complex degradations by leveraging multiple intra- and inter-stage states. First, inspired by the pupil mechanism in the human visual system, we propose Luminance-adaptive Normalization (LAN) that adjusts normalization parameters based on rich inter-stage states, allowing for adaptive, scene-aware luminance modulation. Second, we aggregate multiple intra-stage states through exponential moving average approach, effectively capturing subtle variations while mitigating information loss inherent in the single-state mechanism. To reduce the degradation effects commonly associated with conventional skip connections, we propose the State-aware Selective Fusion (SSF) module, which dynamically aligns and integrates multi-state features across encoder stages, selectively fusing contextual information. In comparison to state-of-the-art models, our URWKV model achieves superior performance on various benchmarks, while requiring significantly fewer parameters and computational resources.

Related papers

• 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)
• ControlFusion: A Controllable Image Fusion Framework with Language-Vision Degradation Prompts [58.99648692413168]
  Current image fusion methods struggle to address the composite degradations encountered in real-world imaging scenarios.<n>We propose ControlFusion, which adaptively neutralizes composite degradations.<n>In experiments, ControlFusion outperforms SOTA fusion methods in fusion quality and degradation handling.
  arXiv  Detail & Related papers  (2025-03-30T08:18:53Z)
• VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression [59.14355576912495]
  NeRF-based video has revolutionized visual media by delivering photorealistic Free-Viewpoint Video (FVV) experiences.<n>The substantial data volumes pose significant challenges for storage and transmission.<n>We propose VRVVC, a novel end-to-end joint variable-rate framework for video compression.
  arXiv  Detail & Related papers  (2024-12-16T01:28:04Z)
• 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)
• 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.<n>We propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework.
  arXiv  Detail & Related papers  (2024-08-10T04:51:43Z)
• Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition [52.89441679581216]
  Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise.<n>We present an innovative video decomposition strategy that incorporates view-independent and view-dependent components.<n>Our framework consistently outperforms existing methods, establishing a new SOTA performance.
  arXiv  Detail & Related papers  (2024-05-24T15:56:40Z)
• Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement [2.9873893715462185]
  We propose a novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED.<n>It utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains.<n>It successfully alleviates the dependence on pairwise training data via zero-reference learning.
  arXiv  Detail & Related papers  (2024-03-05T11:39:17Z)

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.