Abstract
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.
Abstract (translated)
暗图像增强的目的是将暗图像转换为正常光线图像。现有的暗图像增强方法以未压缩的暗图像作为输入，取得了很大的性能。然而，在实践中，在存储或通过互联网传输之前，暗图像通常会被压缩。当前的暗图像处理方法在处理压缩暗图像时表现不佳。被压缩的暗图像中的隐藏 artifacts 被增强，导致观察者产生不舒适的视觉效果。根据这个观察结果，本研究旨在在避免压缩 artifacts 放大的前提下增强压缩暗图像。 由于在压缩暗图像中，纹理细节与压缩 artifacts 在图像空间中交织在一起，因此细节增强和屏蔽 artifacts 抑制在图像空间中是相互矛盾的。因此，我们在潜在空间中处理这个问题。为此，我们提出了一个基于变分自编码器（VAE）的新型潜在映射网络。首先，与仅具有单分辨率特征的之前 VAE 方法不同，我们利用多个潜在空间具有多分辨率特征，以减少细节模糊并提高图像保真度。具体来说，我们训练两个多级 VAE 将压缩暗图像和正常光线图像投影到其潜在空间中。其次，我们利用潜在映射网络将压缩暗空间中的特征转换到正常光线空间。具体来说，由于黑暗和压缩的衰减模型不同，我们将映射过程分为增强明暗分支和抑制阻塞分支。全面的实验证明，与最先进的基于压缩暗图像增强的方法相比，本研究的方法在压缩暗图像增强领域取得了最先进的性能。
URL
https://arxiv.org/abs/2403.07622
