Incomplete Image Completion through GAN

There are two difficult in the existing image restoration methods.One is that the method is difficult to repair the image with a large damaged,the other is the result of image completion is not good and the speed is slow.With the development and application of deep learning,the image repair algorithm based on generative adversarial networks can repair images by simulating the distribution of data.In the process of image completion,the first step is trained the generator to simulate data distribution and generate samples.Then a large number of falsified images are quickly generated using the generative adversarial network and search for the code of the closest damaged image.Finally,the generator generates missing content by using this code.On this basis,this paper combines the semantic loss function and the perceptual loss function.Experimental result show that the method successfully predicts the information of large areas missing in the image,and realizes the photorealism,producing clearer and more consistent results than previous methods.

Image completion algorithm based on texture synthesis

A new algorithm is proposed for completing the missing parts caused by the removal of foreground or background elements from an image of natural scenery in a visually plausible way. The major contributions of the proposed algorithm are： （1） for most natural images, there is a strong orientation of texture or color distribution. So a method is introduced to compute the main direction of the texture and complete the image by limiting the search to one direction to carry out image completion quite fast; （2） there exists a synthesis ordering for image completion. The searching order of the patches is defined to ensure the regions with more known information and the structures should be completed before filling in other regions; （3） to improve the visual effect of texture synthesis, an adaptive scheme is presented to determine the size of the template window for capturing the features of various scales. A number of examples are given to demonstrate the effectiveness of the proposed algorithm.

Panorama completion for street views

This paper considers panorama images used for street views. Their viewing angle of 360° causes pixels at the top and bottom to appear stretched and warped. Although current image completion algorithms work well, they cannot be directly used in the presence of such distortions found in panoramas of street views. We thus propose a novel approach to complete such 360° panoramas using optimizationbased projection to deal with distortions. Experimental results show that our approach is efficient and provides an improvement over standard image completion algorithms.