Image denoising with generative adversarial networks and its application to cell image enhancement

Abstract

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.