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5-Degradation Blind All-in-One Image Restoration

Blind All-in-One Image Restoration aims to remove various degradations from an input image without prior knowledge of the degradation type or severity. In this task, we include 5 of the most common image restoration tasks with five degradations: rain, haze, noise, blur, and low-light conditions. This task focuses on five common image restoration tasks, each addressing a specific degradation: rain , haze, noise, blur, and low-light conditions. For training, we utilize the following datasets: Rain200L for deraining, RESIDE for dehazing, WED and BSD400 for denoising with a noise level of σ=25, GoPro for deblurring, and LoLv1 for low-light enhancement. For evaluation, we employ: Rain100L for deraining, SOTS (outdoor) for dehazing, BSD68 for denoising with σ=25, GoPro for deblurring, and LoLv1 for low-light enhancement. The performance of the models is assessed by reporting the average PSNR across all five evaluation datasets, reflecting the overall capability of the model to handle diverse degradations.

Papers

Showing 18 of 8 papers

TitleStatusHype
Universal Image Restoration Pre-training via Degradation ClassificationCode2
Adaptive Blind All-in-One Image RestorationCode1
Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image RestorationCode3
HAIR: Hypernetworks-based All-in-One Image RestorationCode2
Restore Anything Model via Efficient Degradation AdaptationCode1
Efficient Degradation-aware Any Image Restoration0
Ingredient-Oriented Multi-Degradation Learning for Image RestorationCode1
All-in-One Image Restoration for Unknown CorruptionCode2
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