Image Smear Removal via Improved Conditional GAN and Semantic Network

Abstract

Image inpainting is one of the most important problems in the field of image algorithm, and it is also an effective preprocessing method for many other image applications. In this paper, we suggest an image decontamination method which is mainly used to remove mesh stains and also provide a data set for this task. To our knowledge, this work is the first attempt to solve this kind of problem. Specifically, the proposed method is composed of two phases: we first remove the mesh stains with an Improved Conditional Generative Adversarial Network, and then utilize a Semantic Network to fine tune the details. Experiments demonstrated that this two-stage model can remove the mesh stains. Results show that our method significantly out-performs existing methods and achieves superior inpainting results on challenging cases.