Self-Similarity Sparse Representation for Image Restoration
D Khalandar Basha1, T Venkateswarlu2
1D Khalandar Basha, Research Scholar, Assistant Professor, Department of ECE, Institute of Aeronautical Engineering, SVU College of Engineering, Tirupathi (Andhra Pradesh), India.
2Dr. T Venkateswarlu, Professor, Department of ECE, SVU College of Engineering, Sri Venkateswara University, Tirupathi (Andhra Pradesh), India.
Manuscript received on 13 October 2019 | Revised Manuscript received on 22 October 2019 | Manuscript Published on 02 November 2019 | PP: 1063-1067 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11800982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1180.0982S1119
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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: Image restoration aims to restore an image from a degraded image. The degradation may occur during image acquisition or image transmission. Image degradation lowers the quality of the image. In this paper additive Gaussian noise is considered for degrading the original image. For restoring the image from degraded image the proposed method used both local and non-local similarity patterns. The restoration problem is modeled with regression model. Two regularization terms are considered for representing prior image information. One regularization term is for local patterns and other is for non-local similarity patterns. The additive local regularization term is used to restore the edges. The non-local regularization term works best for local smoothness and edge information will be lost. The proposed algorithm took a clean image of size 256×256 and added with Gaussian noise with different levels of noise levels. A self-adaptive dictionary is trained for a particular window of image with local and non-local patterns and stacked to three dimensional matrix. The patch size considered for training the dictionary is 10×10. For restoring each patch it searches best atoms form the trained dictionary. The efficiency of the algorithm is estimated by parameters mean square error, root mean square error, PSNR and FSIM. The algorithm is also tested for different images like cameraman, house, Barbara, Lena and parrot. The proposed algorithm is tested with conventional algorithms.
Keywords: Restoration, Sparse Representation, Gaussian Noise, Dictionary Learning, Self-Similarity.
Scope of the Article: Image Security