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。

括号是你所需的一切：用多曝光图像统一图像恢复和增强任务

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

Coronary angiography is the gold standard imaging technique for studying and
diagnosing coronary artery disease. However, the resulting 2D X-ray projections
lose 3D information and exhibit visual ambiguities. In this work, we aim to
establish dense correspondence in multi-view angiography, serving as a
fundamental basis for various clinical applications and downstream tasks. To
overcome the challenge of unavailable annotated data, we designed a data
simulation pipeline using 3D Coronary Computed Tomography Angiography (CCTA).
We formulated the problem of dense correspondence estimation as a query
matching task over all points of interest in the given views. We established
point-to-point query matching and advanced it to curve-to-curve correspondence,
significantly reducing errors by minimizing ambiguity and improving topological
awareness. The method was evaluated on a set of 1260 image pairs from different
views across 8 clinically relevant angulation groups, demonstrating compelling
results and indicating the feasibility of establishing dense correspondence in
multi-view angiography.

通过设计数据模拟流程，将密集对应问题转化为所有感兴趣点的查询匹配任务，并提高了拓扑意识，从而建立了多视角病灶密集对应的可行性。