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.

翻译：在低光照环境下获取内容清晰的高质量照片极具挑战性，但又是人们迫切需要的。尽管多图像处理方法（利用连拍、双曝光或多曝光图像）在解决此问题上取得了显著进展，但这些方法通常聚焦于特定的恢复或增强问题，未能充分挖掘多幅图像的潜力。基于多曝光图像在去噪、去模糊、高动态范围成像及超分辨率等任务中的互补特性，本文提出利用曝光包围摄影技术来统一恢复与增强任务。考虑到真实世界数据对难以采集，我们提出一种解决方案：首先使用合成配对数据预训练模型，再将其适配至真实无标签图像。具体而言，我们提出了一种时态调制循环网络（TMRNet）及自监督适配方法。此外，我们构建了数据仿真管线用于合成配对数据，并从200个夜间场景中采集真实图像。在两个数据集上的实验表明，本方法性能优于当前最先进的多图像处理方法。数据集、代码及预训练模型已开源至 https://github.com/cszhilu1998/BracketIRE。