From Physical Degradation Models to Task-Aware All-in-One Image Restoration
- URL: http://arxiv.org/abs/2601.10192v1
- Date: Thu, 15 Jan 2026 08:47:10 GMT
- Title: From Physical Degradation Models to Task-Aware All-in-One Image Restoration
- Authors: Hu Gao, Xiaoning Lei, Xichen Xu, Xingjian Wang, Lizhuang Ma,
- Abstract summary: All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model.<n>We adopt a physical degradation modeling perspective and predict a task-aware inverse degradation operator for efficient all-in-one image restoration.
- Score: 44.45223512440674
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing prompt information or leveraging large models, the added learning modules increase system complexity and hinder real-time applicability. In this paper, we adopt a physical degradation modeling perspective and predict a task-aware inverse degradation operator for efficient all-in-one image restoration. The framework consists of two stages. In the first stage, the predicted inverse operator produces an initial restored image together with an uncertainty perception map that highlights regions difficult to reconstruct, ensuring restoration reliability. In the second stage, the restoration is further refined under the guidance of this uncertainty map. The same inverse operator prediction network is used in both stages, with task-aware parameters introduced after operator prediction to adapt to different degradation tasks. Moreover, by accelerating the convolution of the inverse operator, the proposed method achieves efficient all-in-one image restoration. The resulting tightly integrated architecture, termed OPIR, is extensively validated through experiments, demonstrating superior all-in-one restoration performance while remaining highly competitive on task-aligned restoration.
Related papers
- DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing [73.12011187146481]
Inversion within Diffusion models aims to recover the latent noise representation for a real or generated image.<n>Most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility.<n>We introduce Dual-Conditional Inversion (DCI), a novel framework that jointly conditions on the source prompt and reference image.
arXiv Detail & Related papers (2025-06-03T07:46:44Z) - Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration [109.38288333994407]
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.
arXiv Detail & Related papers (2025-04-14T08:24:57Z) - Proxies for Distortion and Consistency with Applications for Real-World Image Restoration [12.118301237297313]
This paper offers a suite of tools that can serve both the design and assessment of real-world image restoration algorithms.<n>We propose a trained model that predicts the chain of degradations a given real-world measured input has gone through.<n>We show how this estimator can be used to approximate the consistency -- the match between the measurements and any proposed recovered image.
arXiv Detail & Related papers (2025-01-21T12:49:30Z) - Navigating Image Restoration with VAR's Distribution Alignment Prior [6.0648320320309885]
VAR, a novel image generative paradigm, surpasses diffusion models in generation quality by applying a next-scale prediction approach.<n>We formulate the multi-scale latent representations within VAR as the restoration prior, thus advancing our delicately designed VarFormer framework.
arXiv Detail & Related papers (2024-12-30T16:32:55Z) - 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) - EchoIR: Advancing Image Restoration with Echo Upsampling and Bi-Level Optimization [0.0]
We introduce the EchoIR, an UNet-like image restoration network with a bilateral learnable upsampling mechanism to bridge this gap.<n>In pursuit of modeling a hierarchical model of image restoration and upsampling tasks, we propose the Approximated Sequential Bi-level Optimization (AS-BLO)
arXiv Detail & Related papers (2024-12-10T06:27:08Z) - Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration [33.163161549726446]
Perceive-IR is a novel backbone-agnostic All-in-One image restoration framework for fine-grained quality control.<n>Its modular structure allows core components to function independently of specific backbones, enabling seamless integration into advanced restoration models.
arXiv Detail & Related papers (2024-08-28T17:58:54Z) - 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) - 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.