Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks

• URL: http://arxiv.org/abs/2401.00766v4
• Date: Fri, 31 May 2024 14:29:13 GMT
• Title: Exposure Bracketing is All You Need for Unifying Image Restoration and Enhancement Tasks
• Authors: Zhilu Zhang, Shuohao Zhang, Renlong Wu, Zifei Yan, Wangmeng Zuo,
• Abstract summary: We propose to utilize exposure bracketing photography to unify image restoration and enhancement tasks. Due to the difficulty in collecting real-world pairs, we suggest a solution that first pre-trains the model with synthetic paired data. In particular, a temporally modulated recurrent network (TMRNet) and self-supervised adaptation method are proposed.
• Score: 50.822601495422916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, and do not fully explore the potential of utilizing multiple images. Motivated by the fact 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 image 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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