DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model

• URL: http://arxiv.org/abs/2504.17732v2
• Date: Wed, 29 Oct 2025 07:04:04 GMT
• Title: DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model
• Authors: Zhanwen Liu, Sai Zhou, Yuchao Dai, Yang Wang, Yisheng An, Xiangmo Zhao,
• Abstract summary: DPMambaIR is a novel All-in-One image restoration framework that introduces a fine-grained degradation extractor and a Degradation-Aware Prompt State Space Model.<n> experiments show DPMambaIR achieves the best performance, with 27.69dB and 0.893 in PSNR and SSIM, respectively.
• Score: 52.44931846016603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: All-in-One image restoration aims to address multiple image degradation problems using a single model, offering a more practical and versatile solution compared to designing dedicated models for each degradation type. Existing approaches typically rely on Degradation-specific models or coarse-grained degradation prompts to guide image restoration. However, they lack fine-grained modeling of degradation information and face limitations in balancing multi-task conflicts. To overcome these limitations, we propose DPMambaIR, a novel All-in-One image restoration framework that introduces a fine-grained degradation extractor and a Degradation-Aware Prompt State Space Model (DP-SSM). The DP-SSM leverages the fine-grained degradation features captured by the extractor as dynamic prompts, which are then incorporated into the state space modeling process. This enhances the model's adaptability to diverse degradation types, while a complementary High-Frequency Enhancement Block (HEB) recovers local high-frequency details. Extensive experiments on a mixed dataset containing seven degradation types show that DPMambaIR achieves the best performance, with 27.69dB and 0.893 in PSNR and SSIM, respectively. These results highlight the potential and superiority of DPMambaIR as a unified solution for All-in-One image restoration.

Related papers

• 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)
• 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)
• Dynamic Degradation Decomposition Network for All-in-One Image Restoration [3.856518745550605]
  We introduce a dynamic degradation decomposition network for all-in-one image restoration, named D$3$Net.<n>D$3$Net achieves degradation-adaptive image restoration with guided prompt through cross-domain interaction and dynamic degradation decomposition.<n>Experiments on multiple image restoration tasks demonstrate that D$3$Net significantly outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-02-26T11:49:58Z)
• UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity [79.90839080916913]
  We present our UniRestorer with improved restoration performance.<n>Specifically, we perform hierarchical clustering on degradation space, and train a multi-granularity mixture-of-experts (MoE) restoration model.<n>In contrast to existing degradation-agnostic and -aware methods, UniRestorer can leverage degradation estimation to benefit degradation specific restoration.
  arXiv  Detail & Related papers  (2024-12-28T14:09:08Z)
• 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)
• Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers [53.298698981438]
  This paper proposes a new task setting, i.e. Universal Image Restoration (UIR)<n>UIR doesn't require training on all the degradation combinations but only on a set of degradation bases and then removing any degradation that these bases can potentially compose in a zero-shot manner.<n>We propose Chain-of-Restoration (CoR) mechanism, which instructs models to remove unknown composite degradations step-by-step.
  arXiv  Detail & Related papers  (2024-10-11T10:21:42Z)
• UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation [50.27688690379488]
  Existing unified methods treat multi-degradation image restoration as a multi-task learning problem. We propose a universal image restoration framework based on multiple low-rank adapters (LoRA) from multi-domain transfer learning. Our framework leverages the pre-trained generative model as the shared component for multi-degradation restoration and transfers it to specific degradation image restoration tasks.
  arXiv  Detail & Related papers  (2024-09-30T11:16:56Z)
• Training-Free Large Model Priors for Multiple-in-One Image Restoration [24.230376300759573]
  Large Model Driven Image Restoration framework (LMDIR) Our architecture comprises a query-based prompt encoder, degradation-aware transformer block injecting global degradation knowledge. This design facilitates single-stage training paradigm to address various degradations while supporting both automatic and user-guided restoration.
  arXiv  Detail & Related papers  (2024-07-18T05:40:32Z)
• Efficient Degradation-aware Any Image Restoration [83.92870105933679]
  We propose textitDaAIR, an efficient All-in-One image restorer employing a Degradation-aware Learner (DaLe) in the low-rank regime. By dynamically allocating model capacity to input degradations, we realize an efficient restorer integrating holistic and specific learning.
  arXiv  Detail & Related papers  (2024-05-24T11:53:27Z)
• Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models [14.25759541950917]
  This work leverages a capable vision-language model and a synthetic degradation pipeline to learn image restoration in the wild (wild IR) Our base diffusion model is the image restoration SDE (IR-SDE)
  arXiv  Detail & Related papers  (2024-04-15T12:34:21Z)
• Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts [52.39959535724677]
  We introduce an alternative solution to improve the generalization of image restoration models. We propose AdaptIR, a Mixture-of-Experts (MoE) with multi-branch design to capture local, global, and channel representation bases. Our AdaptIR achieves stable performance on single-degradation tasks, and excels in hybrid-degradation tasks, with fine-tuning only 0.6% parameters for 8 hours.
  arXiv  Detail & Related papers  (2023-12-12T14:27:59Z)
• DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration [66.01846902242355]
  Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training. It is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. We propose Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image.
  arXiv  Detail & Related papers  (2023-03-13T06:05:18Z)
• Super-resolution Reconstruction of Single Image for Latent features [8.857209365343646]
  Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. It is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling.
  arXiv  Detail & Related papers  (2022-11-16T09:37:07Z)
• One Size Fits All: Hypernetwork for Tunable Image Restoration [5.33024001730262]
  We introduce a novel approach for tunable image restoration that achieves the accuracy of multiple models, each optimized for a different level of degradation. Our model can be optimized to restore as many degradation levels as required with a constant number of parameters and for various image restoration tasks.
  arXiv  Detail & Related papers  (2022-06-13T08:33:14Z)

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.