Hierarchical Approach for Total Variation Digital Image Inpainting
This addresses the challenge of digital image inpainting for large damaged areas, which existing methods often fail to reconstruct properly, making it an incremental improvement for image processing applications.
The paper tackles the problem of reconstructing damaged image regions automatically, proposing a hierarchical method that reduces the area in multiple levels and uses Total Variation inpainting at each level, resulting in better performance compared to existing algorithms like nearest neighbor interpolation, Inpainting through Blurring, and Sobolev Inpainting.
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.