Multiview Scene Image Inpainting Based on Conditional Generative Adversarial Networks

Abstract

With the help of a multiview system, an unmanned vehicle system can better understand the surrounding environment and choose a more accurate and safer path to avoid obstacles. However, due to the interference of the signal or the loss of part of the signal during the acquisition, processing, compression, transmission and decompression of the video image signal, the local area of the image is abnormal, which affects the perception and decision of the system. This article addresses the problems of inaccurate restored images and noise in the restored images by proposing an image restoration method that is applied to a multicamera system. We utilize different perspective images captured by different cameras to assist and constrain the restoration of the damaged image. This method restores the image by combining sample representations and sample distribution models which respectively based on self-encoder reconstruction loss learning and generative adversarial networks. In this method, the infrastructure is a conditional generative adversarial network, the condition is the images that are from the other perspectives, and the generator is a self-encoder structure with cross-layer connection, group convolution and feature map channel exchanged. This method was carried out on a dataset recorded in Zurich using a pair of cameras mounted on a mobile platform. The experimental results demonstrate that the proposed method is superior to the existing methods in terms of mean L1 Loss, mean L2 Loss and the peak signal to noise ratio (PSNR).