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Image inpainting refers to filling missing places in images using neighboringpixels. It also has many applications in different tasks of image processing.Most of these applications enhance the image quality by significant unwantedchanges or even elimination of some existing pixels. These changes requireconsiderable computational complexities which in turn results in remarkableprocessing time. In this paper we propose a fast inpainting algorithm calledRIBBONS based on selection of patches around each missing pixel. This wouldaccelerate the execution speed and the capability of online frame inpainting invideo. The applied cost-function is a combination of statistical and spatialfeatures in all neighboring pixels. We evaluate some candidate patches usingthe proposed cost function and minimize it to achieve the final patch.Experimental results show the higher speed of 'Ribbons' in comparison withprevious methods while being comparable in terms of PSNR and SSIM for theimages in MISC dataset.

RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics