Publication Type : Conference Proceedings
Publisher : Computational Vision and Bio-Inspired Computing, Springer International Publishing,
Source : Computational Vision and Bio-Inspired Computing, Springer International Publishing, Volume 1108, Cham, p.809-826 (2020)
Url : https://link.springer.com/chapter/10.1007/978-3-030-37218-7_89
ISBN : 9783030372187
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2020
Abstract : In the past few decades, denoising is one of the major portions in imaging analysis and it is still an ongoing research problem. Depending upon some pursuit methods an attempt has been made to denoise an image. The work comes up with a new methodology for denoising with K-SVD algorithm. Noise information has been extracted using the proposed approach. With reference to heap sort image patches are learnt using dictionary and then it is updated. Experimentation says that introduced approach reduces noise on test. The proposed approach is tested on test datasets and the proposed approach is found to be comparatively good than the existing works.
Cite this Research Publication : S. Kumar Thangavel and Rudra, S., “A Sparse Representation Based Learning Algorithm for Denoising in Images”, Computational Vision and Bio-Inspired Computing, vol. 1108. Springer International Publishing, Cham, pp. 809-826, 2020.