Image De-noising using Optimized Self Similar Patch based Filter
1A.Gayathri*, Associate Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, India.
2S.Christy, Assistant Professor (SG), Department of Information Technology, Saveetha School of Engineering, SIMATS, Chennai, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1570-1578 | Volume-8 Issue-12, October 2019. | Retrieval Number: L31311081219/2019©BEIESP | DOI: 10.35940/ijitee.L3131.1081219
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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: Emerging trends in the widespread use of technology has led to proliferation of images and videos acquired and distributed through electronic devices. There is an increasing need towards capturing high fidelity images and filtering of the concomitant noise inevitable in the capture, transmission and reception of the same. In this paper, we propose an OPSS (Optimized Patch based Self Similar) filter that exploits concurrently the photometric, geometric and graphical patch similarities of the image. This model recognizes similar patches to segregate the corrupted from the uncorrupted pixels in an image and improve the performance of denoising. Photometric patch similarity is established by using Non-Local Means Decision Based Unsymmetrical Trimmed Median (NLM-DBUTM) filter, which computes weights based on the reference patch. The geometrical patch similarity is done through the K-means clustering and graphically similar patches are identified through Ant Colony Optimization (ACO) technique. These “three similarities” based models have been taken advantage of and combined to arrive at a more comprehensive and effective denoising. The results obtained through the OPSS algorithm demonstrate improved efficiency in removing Gaussian and Impulse noise. Experimental results demonstrate that our proposed study achieves good performance with respect to other denoising algorithms being compared. Experimental results are based on performance measure (evaluation parameters) through Peak Signal to Noise Ratio (PSNR), Mean squared error (MSE) and Structural Similarity Index Measure (SSIM).
Keywords: Ant Colony Optimization (ACO), Image Denoising, Non Local Means-Decision Based Un-Symmetric Trimmed Median (NLM-DBUTM), Locally Adaptive Regression Kernels (LARKs), Optimized Self Similar Patch based Filter (OPSS).
Scope of the Article: Regression and Prediction