Image Inpainting

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Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and texture, but often create distorted structures or blurry textures in-consistent with surrounding areas. Image inpainting is an application of image reconstruction many research and work has been done in this domain that mainly contained Generative adversarial networks and partial convolution networks. Both approach has made significant changes to the problem area of image inpainting. When depending solely on convolutional neural network (CNN) or adversarial supervision, plausible in-painting results cannot be guaranteed because of the irregular holes. Moreover one particular approach cannot help with generating robust and realistic output. That leads to the idea of implementing the Generative adversarial networks with auto encoder decoder approach. Both models facilitate refined output filling the holes that looked more realistic and we could combine them into a user interface so that one can compare the results. A comparison based study of both the models implementation training on different data sets and analysing the outputs has been proposed.

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