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 get a high-quality image by combining these 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. Code and datasets 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具有强烈需求但极具挑战性。尽管多图像处理方法（使用连拍、双曝光或多曝光图像）在解决此问题上已取得显著进展，但这些方法通常专注于特定的复原或增强问题，并未充分挖掘利用多幅图像的潜力。受多曝光图像在去噪、去模糊、高动态范围成像和超分辨率任务中具有互补性这一事实的启发，本研究提出通过结合这些任务，利用曝光包围摄影技术来获得高质量图像。由于收集真实世界配对数据存在困难，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，然后将其适配到真实世界的无标签图像。具体而言，我们提出了时序调制循环网络（TMRNet）和自监督适配方法。此外，我们构建了数据模拟流程来合成配对数据，并从200个夜间场景中收集了真实世界图像。在两个数据集上的实验表明，我们的方法性能优于当前最先进的多图像处理方法。代码和数据集可在 https://github.com/cszhilu1998/BracketIRE 获取。