RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics

Image inpainting refers to filling missing places in images using neighboring pixels. It also has many applications in different tasks of image processing. Most of these applications enhance the image quality by significant unwanted changes or even elimination of some existing pixels. These changes require considerable computational complexities which in turn results in remarkable processing time. In this paper, we propose a fast inpainting algorithm called RIBBONS based on the selection of patches around each missing pixel. Patch selection would accelerate the execution speed and the capability of online frame inpainting in the video. The applied cost-function is a combination of statistical and spatial features in all neighboring pixels. We evaluate some candidate patches using the proposed cost function and minimize it to achieve the final patch. Experimental results show the higher speed of RIBBONS method in comparison with previous methods while being comparable in terms of PSNR and SSIM for the images in MISC dataset.