A key part of interpreting, visualizing, and monitoring the surface conditions of remote-sensing images is enhancing the quality of low-light images. It aims to produce higher contrast, noise-suppressed, and better quality images from the low-light version. Recently, Retinex theory-based enhancement methods have gained a lot of attention because of their robustness. In this study, Retinex-based low-light enhancement methods are compared to other state-of-the-art low-light enhancement methods to determine their generalization ability and computational costs. Different commonly used test datasets covering different content and lighting conditions are used to compare the robustness of Retinex-based methods and other low-light enhancement techniques. Different evaluation metrics are used to compare the results, and an average ranking system is suggested to rank the enhancement methods.

This paper proposes an image denoising training framework based on Wasserstein Generative Adversarial Networks (WGAN) and applies it to cell image denoising. Cell image denoising is a challenging task which has high requirement on the recovery of feature details. Current popular convolutional neural network (CNN) based denoising methods encounter a blurriness issue that denoised images are blurry on texture details, which is fatal for the cell image denoising. In this paper, to solve the blurriness issue, we first theoretically analyze the cause of the blurriness issue. Subsequently, an image denoising training framework with WGAN based adversarial learning is proposed. This training framework solves the blurriness issue by guiding the denoising network to find the distribution space of real clean images rather than the distribution space of blurry images and introducing feature information. Experimental results show that this training framework can effectively solve the blurriness issue and achieve better denoising performance than the state-of-the-art denoising methods. Meanwhile, the application of this training framework on cell image denoising also achieves satisfactory performance. Recovered cell images of this training framework are clear on feature details.