Images are widely used over various applications under the aegis of various domains like Computer vision, Biomedical, etc. The problem of missing data identification is of great concern in various fields involving image processing. Least square can be used for missing sample estimation for 1-D signals. The proposed system extends the missing sample estimation in 1-D using least square to 2-D, applied for image inpainting. The paper also draws a comparison between the Total Variation (TV) algorithm and the proposed method. The experiments were conducted on standard images and the standard metrics namely PSNR and SSIM are used to compare the image quality obtained using the proposed method (least square based) and TV algorithm.
A. M, N, D., Sowmya V., Mahan, N., and Dr. Soman K. P., “Least Square Based approach for Image Inpainting”, Institute of Integrative Omics and Applied Biotechnology, vol. 7, pp. 44-59, 2016.