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It is highly desired but challenging to acquire high-quality photos withclear content in low-light environments. Although multi-image processingmethods (using burst, dual-exposure, or multi-exposure images) have madesignificant progress in addressing this issue, they typically focus on specificrestoration or enhancement problems, and do not fully explore the potential ofutilizing multiple images. Motivated by the fact that multi-exposure images arecomplementary in denoising, deblurring, high dynamic range imaging, andsuper-resolution, we propose to utilize exposure bracketing photography to geta high-quality image by combining these tasks in this work. Due to thedifficulty in collecting real-world pairs, we suggest a solution that firstpre-trains the model with synthetic paired data and then adapts it toreal-world unlabeled images. In particular, a temporally modulated recurrentnetwork (TMRNet) and self-supervised adaptation method are proposed. Moreover,we construct a data simulation pipeline to synthesize pairs and collectreal-world images from 200 nighttime scenarios. Experiments on both datasetsshow that our method performs favorably against the state-of-the-artmulti-image processing ones. Code and datasets are available athttps://github.com/cszhilu1998/BracketIRE.

Exposure Bracketing Is All You Need For A High-Quality Image