Image Restoration by Linear Regression for Gaussian Noise Removal from Natural Images
D Khalandar Basha1, T Venkateswarlu2
1D Khalandar Basha, Research Scholar, Department of ECE, SVU College of Engineering, Tirupati (Andhra Pradesh), India.
2Dr. T Venkateswarlu, Professor, Department of ECE, SVU College of Engineering, Sri Venkateswara University, Tirupati (Andhra Pradesh), India.
Manuscript received on 05 September 2019 | Revised Manuscript received on 14 September 2019 | Manuscript Published on 26 October 2019 | PP: 126-130 | Volume-8 Issue-11S2 September 2019 | Retrieval Number: K102009811S219/2019©BEIESP | DOI: 10.35940/ijitee.K1020.09811S219
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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 improves the features information of degraded or corrupted image. The degradation of image because of addition of noise when acquiring the image. Many algorithms are developed by many researches. In this paper image is corrupted by Gaussian noise to generate degraded image. The image is restored from this degraded image by supervised learning based algorithm. Few images are considered for training the dictionary with each element of size 9×9. The degraded image is considered patch by patch for restoring the patch from the trained set of images by support vector machine. The quality assessment of the image done by comparing the quality matrices like mean square error, root mean square error, peak signal to noise ratio, structural similarity index measure and feature similarity index measure. In this paper the images are considered are cameraman, house, Lena, Barbara and Parrot.
Keywords: Image Restoration, Dictionary Learning, About Six Key Words Separated by Commas (Minimum 4 Key words).
Scope of the Article: Image Security