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It is challenging but highly desired 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 exclusivelyon specific restoration or enhancement tasks, being insufficient in exploitingmulti-image. Motivated by that multi-exposure images are complementary indenoising, deblurring, high dynamic range imaging, and super-resolution, wepropose to utilize bracketing photography to unify restoration and enhancementtasks in this work. Due to the difficulty in collecting real-world pairs, wesuggest a solution that first pre-trains the model with synthetic paired dataand then adapts it to real-world unlabeled images. In particular, a temporallymodulated recurrent network (TMRNet) and self-supervised adaptation method areproposed. Moreover, we construct a data simulation pipeline to synthesize pairsand collect real-world images from 200 nighttime scenarios. Experiments on bothdatasets show that our method performs favorably against the state-of-the-artmulti-image processing ones. The dataset, code, and pre-trained models areavailable at https://github.com/cszhilu1998/BracketIRE.

Bracketing is All You Need: Unifying Image Restoration and Enhancement Tasks with Multi-Exposure Images