An Optimal Patch Size based Sporadic Decomposition of Hankel Structured Matrix in Gradient Transform Domain for Impulse Noise Denoising - Victoria, L. Baby; Sathappan, S (International Journal of Computer Sciences and Engineering, 2018)
Noise removal refers to the most vital process in image processing to remove the noise from a given image and reconstruct the original image. Among many denoising techniques, four types of extended versions of robust Annihilating filter-based Low-rank Hankel Matrix (r-ALOHA) approaches have been proposed in the previous researches. In those approaches, different kinds of transform domains like log-exponential, wavelet, generalized Hough, and gradient were considered separately in which that the image patch was considered as it was sparse in the considered transform domains independently to denoise the corrupted image. Even if gradient transform based denoising called e4-ALOHA achieves better performance than the other transform domains, it requires an automatic selection of Optimal Patch Size (OPS) to further improve the denoising performance. Hence in this article, an automatic selection of OPS is proposed with e4-ALOHA that searches similar image patches and selects an optimal patch size. In this technique, a Flower Pollination optimization Algorithm (FPA) is proposed to search similar patches and choose an optimal patch size adaptively according to the variance of similar patch groups. Once an optimal patch size is selected, e4-ALOHA is applied to perform the denoising process. Finally, the effectiveness of the proposed technique is evaluated through the experimental results.