Hierarchical Approach for Total Variation Digital Image Inpainting

TL;DR

This paper introduces a hierarchical total variation inpainting method that improves the reconstruction quality of larger damaged regions in digital images by reducing the inpainting area across multiple levels.

Contribution

The paper proposes a novel hierarchical approach combined with total variation inpainting, enhancing performance over existing methods for larger damaged regions.

Findings

Outperforms existing inpainting algorithms for larger regions

Improves reconstruction quality with hierarchical reduction

Demonstrates faster and more accurate inpainting results

Abstract

The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consuming process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.