Patch sparsity based image inpainting using local patch statistics and steering kernel descriptor
Document Type
Conference Article
Publication Title
Proceedings - International Conference on Pattern Recognition
Abstract
This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further we extract local steering kernel (LSK) based feature to preserve geometric structure and texture sharpness in the target region. The advantage of non local self similarity as redundancy of similar patches in natural images is introduced to find the candidate patches from the whole source region. Based on these local and non local prior information we propose a sparse representation framework for image inpainting. Our proposed method is tested on wide range of natural images. The experimental results show the superiority of the proposed method compared to some of the previous approaches.
First Page
781
Last Page
786
DOI
10.1109/ICPR.2016.7899730
Publication Date
1-1-2016
Recommended Citation
Ghorai, Mrinmoy; Mandal, Sekhar; and Chanda, Bhabatosh, "Patch sparsity based image inpainting using local patch statistics and steering kernel descriptor" (2016). Conference Articles. 774.
https://digitalcommons.isical.ac.in/conf-articles/774
