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

Merging multi-exposure images is a common approach for obtaining high dynamic
range (HDR) images, with the primary challenge being the avoidance of ghosting
artifacts in dynamic scenes. Recent methods have proposed using deep neural
networks for deghosting. However, the methods typically rely on sufficient data
with HDR ground-truths, which are difficult and costly to collect. In this
work, to eliminate the need for labeled data, we propose SelfHDR, a
self-supervised HDR reconstruction method that only requires dynamic
multi-exposure images during training. Specifically, SelfHDR learns a
reconstruction network under the supervision of two complementary components,
which can be constructed from multi-exposure images and focus on HDR color as
well as structure, respectively. The color component is estimated from aligned
multi-exposure images, while the structure one is generated through a
structure-focused network that is supervised by the color component and an
input reference (\eg, medium-exposure) image. During testing, the learned
reconstruction network is directly deployed to predict an HDR image.
Experiments on real-world images demonstrate our SelfHDR achieves superior
results against the state-of-the-art self-supervised methods, and comparable
performance to supervised ones. Codes are available at
this https URL

该研究提出了一种自监督的高动态范围（HDR）重建方法 SelfHDR，该方法仅需使用动态多曝光图像进行训练，可消除对有标签数据的需求，并通过两个互补组件来学习重建网络，分别关注 HDR 颜色和结构。实验结果表明，SelfHDR 在真实世界图像上优于最先进的自监督方法，并与有监督方法具有可比性。

自我监督的动态场景中使用多曝光图像的高动态范围成像

Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in  Dynamic Scenes

In this paper, we propose TransMEF, a transformer-based multi-exposure image
fusion framework that uses self-supervised multi-task learning. The framework
is based on an encoder-decoder network, which can be trained on large natural
image datasets and does not require ground truth fusion images. We design three
self-supervised reconstruction tasks according to the characteristics of
multi-exposure images and conduct these tasks simultaneously using multi-task
learning; through this process, the network can learn the characteristics of
multi-exposure images and extract more generalized features. In addition, to
compensate for the defect in establishing long-range dependencies in CNN-based
architectures, we design an encoder that combines a CNN module with a
transformer module. This combination enables the network to focus on both local
and global information. We evaluated our method and compared it to 11
competitive traditional and deep learning-based methods on the latest released
multi-exposure image fusion benchmark dataset, and our method achieved the best
performance in both subjective and objective evaluations.

本文提出了一种基于 Transformer 的多曝光图像融合框架 TransMEF，使用自监督多任务学习，通过设计重构任务并使用多任务学习进行同时训练，使网络可以学习到多曝光图像的特征和提取更广义的特征，同时将 CNN 模块与 Transformer 模块结合来解决 CNN-based 构架中建立长距离依赖的缺陷。在对比 11 种传统和深度学习方法后，本文提出的方法在主观和客观评估中都取得了最佳性能。