Dark image enhancement aims at converting dark images to normal-light images. Existing dark image enhancement methods take uncompressed dark images as inputs and achieve great performance. However, in practice, dark images are often compressed before storage or transmission over the Internet. Current methods get poor performance when processing compressed dark images. Artifacts hidden in the dark regions are amplified by current methods, which results in uncomfortable visual effects for observers. Based on this observation, this study aims at enhancing compressed dark images while avoiding compression artifacts amplification. Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space. Therefore, we handle the task in latent space. To this end, we propose a novel latent mapping network based on variational auto-encoder (VAE). Firstly, different from previous VAE-based methods with single-resolution features only, we exploit multiple latent spaces with multi-resolution features, to reduce the detail blur and improve image fidelity. Specifically, we train two multi-level VAEs to project compressed dark images and normal-light images into their latent spaces respectively. Secondly, we leverage a latent mapping network to transform features from compressed dark space to normal-light space. Specifically, since the degradation models of darkness and compression are different from each other, the latent mapping process is divided mapping into enlightening branch and deblocking branch. Comprehensive experiments demonstrate that the proposed method achieves state-of-the-art performance in compressed dark image enhancement.

翻译：暗图像增强旨在将暗图像转换为正常光照图像。现有暗图像增强方法以未压缩的暗图像为输入，取得了卓越性能。然而在实际应用中，暗图像在存储或互联网传输前往往会被压缩。当前方法处理压缩暗图像时性能较差。暗区域中隐藏的伪影会被现有方法放大，导致观感不适。基于这一观察，本研究旨在增强压缩暗图像的同时避免压缩伪影放大。由于纹理细节与压缩伪影在压缩暗图像中相互交织，细节增强与块效应抑制在图像空间中相互矛盾。因此，我们在潜空间中处理该任务。为此，我们提出一种基于变分自编码器的新型潜映射网络。首先，与以往仅使用单分辨率特征的VAE方法不同，我们利用多分辨率特征的多重潜空间，以减少细节模糊并提升图像保真度。具体而言，我们训练两个多级VAE，分别将压缩暗图像和正常光照图像投影到各自的潜空间。其次，我们利用潜映射网络将特征从压缩暗空间转换到正常光照空间。具体来说，鉴于暗化与压缩的退化模型互不相同，潜映射过程被分解为光照增强分支和去块效应分支。综合实验表明，所提方法在压缩暗图像增强方面达到了最优性能。