A Research for Implementing Image Interpolation Using Inpainting and Shearlet Transform
P.J. Sajith1, S.U. Aswathy2, Bibin Vincent3, R.S. Anoop Sreekumar4
1P. J. Sajith, Assistant Professor, Department of Computer Science and Engineering, Vijnan Institute of Science and Technology, Ernakulam, (Kerala), India.
2Dr. S.U. Aswathy, Professor, Department of Computer Science and Engineering, Mangalam College of Engineering, Ettumanoor, Kottayam (Kerala), India.
3Dr. Bibin Vincent, Professor, Department of Computer Science and Engineering, Mangalam College of Engineering, Ettumanoor, Kottayam (Kerala), India.
4Dr. R.S. Anoop Sreekumar, Assistant Professor, Department of Computer Science, Malankara Catholic College, Mariagiri, Kanyakumari (Tamil Nadu), India.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 518-521 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10800982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1080.0982S1119
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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: This paper proposes a way to improve the compression ratio of images by expunging some parts of the image prior transmission. The remaining data besides essential details for recovering the removed regions are encoded to produce the final data. At the decoding side an inpainting method is applied to retrieve the removed region. The Shearlet Transform is used for the smoothing purpose of the recovered image. This transform can identify the location of singularities of a function and also the orientation of discontinuity curves. The Shearlet Transform has the ability to provide a very accurate geometrical characterization of general discontinuity occurring in images.
Keywords: Image Inpainting, Image Compression, Compression Ratio, Peak Signal-to-Noise Ratio, Structural Similarity.
Scope of the Article: Image Security