Image Denoising & Metric Parameters Improvement using Dictionary Learning and Sparse Coding
Bhagya Prasad Bugge1, Bh.S.S.D.S Nagendra Varma2, A.Amarnath3
1Bhagya Prasad Bugge, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
2Bh.S.S.D.S Nagendra Varma, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
3A.Amarnath, Department of Electronics and Communication Engineering, SRKR Engineering College, Bhimavaram (Andhra Pradesh), India.
Manuscript received on 11 May 2019 | Revised Manuscript received on 05 June 2019 | Manuscript Published on 15 June 2019 | PP: 1-5 | Volume-8 Issue-1S3 June 2019 | Retrieval Number: A10010681S319/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Digital image processing uses efficient computer algorithms for image denoising and to improve the image quality. Noisy image is produced due to various reasons in image acquisition, compression, preprocessing, segmentation etc. Over the last decade, various methods have shown promising results in removing zero mean Gaussian noise from images. Apart from different strategies implemented for noise reduction; this paper proposes a method for reducing noise and to improve metric parameters. Without using pre-chosen set of basis functions to represent the image, this paper discuss about performing image denoising using dictionary learning and sparse representation. Instead of removing coefficients of noise, shrinking sparse coefficients of noise is implemented to eliminate noise and it retains the image quality.
Keywords: Terms: Denoising, Dictionary Learning, Sparse Representation, Shrinkage Map Design.
Scope of the Article: Image Security