Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks
arxiv(2024)
摘要
It is highly desired but challenging to acquire high-quality photos with
clear content in low-light environments. Although multi-image processing
methods (using burst, dual-exposure, or multi-exposure images) have made
significant progress in addressing this issue, they typically focus on specific
restoration or enhancement problems, being insufficient in exploiting
multi-image. Motivated by that multi-exposure images are complementary in
denoising, deblurring, high dynamic range imaging, and super-resolution, we
propose to utilize exposure bracketing photography to unify restoration and
enhancement tasks in this work. Due to the difficulty in collecting real-world
pairs, we suggest a solution that first pre-trains the model with synthetic
paired data and then adapts it to real-world unlabeled images. In particular, a
temporally modulated recurrent network (TMRNet) and self-supervised adaptation
method are proposed. Moreover, we construct a data simulation pipeline to
synthesize pairs and collect real-world images from 200 nighttime scenarios.
Experiments on both datasets show that our method performs favorably against
the state-of-the-art multi-image processing ones. The dataset, code, and
pre-trained models are available at https://github.com/cszhilu1998/BracketIRE.
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