Non Blinded Image Inpainting With Low Rank Non-Negative Matrix Factorization

Abstract

In digital image processing object removal from a digital image is termed inpainting. Image recuperation especially with high curvature is inpainting challenges. Due to both colour and texture preservation non blinded exemplar-based approaches are famous among other inpainting algorithms. In general, these algorithm possess a greedy approach in the selection of a target patch, the selection is based upon dedicated priority defined on every patch at contour of the target region, this may jeopardize searching and selection of a good exemplar in global space. The whole process is time consuming and computationally expensive. To tackle said problems a novel algorithm is proposed which reduces the dimension of global search space using neighbour patches information of target region throughNon-Negative Matrix Factorization (NMF). Due to NMF nature of lower dimension approximation, improvised efficiency is observed in an experiment without compromising the quality of inpainted image. The inpainted result of the proposed algorithm is comparable with other inpaintingtechniques.