3260 papers • 126 benchmarks • 313 datasets
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.
(Image credit: Open Source)
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