RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics

TL;DR

RIBBONS is a fast image inpainting algorithm that uses neighborhood statistics to efficiently fill missing image regions, suitable for real-time video applications.

Contribution

The paper introduces RIBBONS, a novel inpainting method that significantly accelerates processing by selecting patches based on combined statistical and spatial features.

Findings

Faster inpainting compared to previous methods

Maintains comparable PSNR and SSIM metrics

Effective for online video frame inpainting

Abstract

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 selection of patches around each missing pixel. This would accelerate the execution speed and the capability of online frame inpainting in 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' in comparison with previous methods while being comparable in terms of PSNR and SSIM for the images in MISC dataset.