College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2023 Jan 10;23(2):792. doi: 10.3390/s23020792.
Due to the influence of poor lighting conditions and the limitations of existing imaging equipment, captured low-illumination images produce noise, artifacts, darkening, and other unpleasant visual problems. Such problems will have an adverse impact on the following high-level image understanding tasks. To overcome this, a two-stage network is proposed in this paper for better restoring low-illumination images. Specifically, instead of manipulating the raw input directly, our network first decomposes the low-illumination image into three different maps (i.e., reflectance, illumination, and feature) via a Decom-Net. During the decomposition process, only reflectance and illumination are further denoised to suppress the effect of noise, while the feature is preserved to reduce the loss of image details. Subsequently, the illumination is deeply adjusted via another well-designed subnetwork called Enhance-Net. Finally, the three restored maps are fused together to generate the final enhanced output. The entire proposed network is optimized in a zero-shot fashion using a newly introduced loss function. Experimental results demonstrate that the proposed network achieves better performance in terms of both objective evaluation and visual quality.
由于照明条件差和现有成像设备的限制，拍摄到的低光照图像会产生噪声、伪影、变暗等不良视觉问题。这些问题将对以下高级图像理解任务产生不利影响。为了克服这个问题，本文提出了一种两阶段网络，用于更好地恢复低光照图像。具体来说，我们的网络不是直接操作原始输入，而是首先通过 Decom-Net 将低光照图像分解为三个不同的图（即反射率、光照和特征）。在分解过程中，仅对反射率和光照进行进一步去噪以抑制噪声的影响，同时保留特征以减少图像细节的损失。然后，通过另一个名为 Enhance-Net 的精心设计的子网对光照进行深度调整。最后，将三个恢复的图融合在一起生成最终的增强输出。整个所提出的网络使用新引入的损失函数以零镜头方式进行优化。实验结果表明，所提出的网络在客观评估和视觉质量方面都取得了更好的性能。
