Blind inpainting and super-resolution using convolutional neural network

Abstract

In this work, we propose a combined approach to two image processing problems:Image Inpainting and Image Super-Resolution(SR). A number of efficient techniqueshave been developed for solving these two problems using deep learning,separately. Researchers have developed hierarchical approaches to solve theseproblems, first in-paint and then super-resolve but there is not much advancementfor solving them simultaneously. There are many applications where both inpaintingand super-resolution are desired simultaneously like digital reconstructionof invaluable artwork in heritage sites, immersive walk-through systems etc.We present a supervised learning based approach for simultaneous blind inpaintingand super-resolution using Deep Convolutional Neural Network. Networklearns mapping between corrupted image patches and true image patches as wellas mapping from low resolution features to high resolution features. Trained deepconvolutional neural network accepts corrupted low resolution (LR) image as inputand outputs a clean high resolution (HR) image. Our network is capable of removingcomplex patterns from an image and providing higher resolution. However,our focus is limited to simultaneous scratch inpainting and super-resolution.