Author : Roshani Parashram Sonawane

Various factors affect the image that causes image deterioration. The art of restoration of deteriorated parts of image is known as image inpainting.  The proposed system focuses on context-aware patch-based image inpainting.  The proposed system is Exemplar-based inpainting solution. Initially the image is divided in variable size block according to their context. The candidate patches are searched with the matched image block. The Markov random field model is used to manage the access of nearest matching patches.  The image inpainting is based on surface fitting as the prior knowledge. This technique uses the angle-aware patch matching. For the matching precision between patches, the Jaccard similarity coefficient is used. This maintains the consistency of the structures and textures. The system is tested on multiple dataset images for object removal. For image quality analysis, PSNR ratio is calculated. The system results are compared with existing systems in terms of efficiency.

Keywords: Image inpainting, Surface fitting, Angle awareness, Dynamic patch selection, Markov random field model