It is challenging but highly desired 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 exclusively
on specific restoration or enhancement tasks, 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 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 this https URL

在低光环境中获取内容清晰的高质量照片具有挑战性但极为理想。我们提出利用曝光合成摄影来统一恢复和增强任务，通过模拟数据与自适应方法，克服了收集真实场景对方法进行训练的困难。实验结果表明，我们的方法在两个数据集上相对于最先进的多图像处理方法表现出色。