PEDESTRIAN IMAGE INPAINTING BASED ON GENERATIVE ADVERSARIAL NETWORK ARCHITECTURE

Abstract

The research of image defect repair aims to automatically repair the defect content in the image through the computer. In recent years, the emergence of deep neural network technology has effectively promoted the development of image restoration technology. This article focuses on the subject of pedestrian image restoration. Today, surveillance cameras have been widely deployed in every corner of the city. Therefore, huge pedestrian images can be acquired every second. Therefore, how to automatically analyze and understand its basic content has become an urgent research topic, which has obvious theoretical and practical value. Obviously, the quality of the source image will seriously affect the subsequent stage of understanding. Therefore, this article will discuss how to recover damaged pedestrian images. In order to recover robustly, we adopted an adversarial generation framework.