Satellite Image Denoising Based on Entropy Thresholding using Shearlet Transform
Anju T S1, Nelwin Raj N R2
1Anju T S, Department of Electronics and Communication Engineering, Sree Chitra Thirunal College of Engineering, Trivandrum (Kerala), India.
2Nelwin Raj N R, Assistant Professor, Department of Electronics and Communication Engineering, Sree Chitra Thirunal College of Engineering, Trivandrum (Kerala), India.
Manuscript received on 13 June 2016 | Revised Manuscript received on 20 June 2016 | Manuscript Published on 30 June 2016 | PP: 45-48 | Volume-5 Issue-5, June 2016 | Retrieval Number: E4602065516/16©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: Satellite images have become universal standard in almost all applications of image processing. However, satellite images are susceptible to noise arising due to unresolved flaws in acquisition and transmission system. Development of a denoising algorithm in satellite images is still a challenging task for many researchers. Most of the state of the art denoising schemes employ wavelet transform but the main limitation of wavelet transform is that it can preserve only point singularity. Shearlet transformation is a sparse, multiscale and multidimensional alternative to wavelet transform. Shearlet transform is optimal in representing image containing edges. In this paper, a novel image denoising algorithm utilizing shearlet transform and entropy thresholding is presented which was found to exhibit superior performance among other state of the art image denoising algorithms in terms of peak signal to noise ratio (PSNR).T
Keywords: Denoising, Discrete Shearlet Transform, Entropy Thresholding
Scope of the Article: Discrete Optimization