Mask-Nha Based Image Denoising with Random Walker Segmentation
K.M. Hemam Baran1, S.A.K. Jilani2
1K.M. Hemam Baran, Research Scholar, Department of ECE, Rayalaseema University, Kurnool (A.P), India.
2Dr. S.A.K. Jilani, Professor, MITS, Madanapalle (A.P), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 04 April 2019 | Manuscript Published on 18 April 2019 | PP: 456-461 | Volume-7 Issue-6S March 2019 | Retrieval Number: F02880376S19/2019©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: The search for well-organized image de-noising techniques is still a valid challenge at the crossing of functional learning and statistics. In spite of the refinement of the currently proposed methods, most algorithms have not yet succeeded a desirable level of applicability. In order to reduce the drawbacks in the earlier methods, a novel algorithm probabilistic method is associated as two-dimensional non-harmonic analysis called mask non-harmonic analysis such a way that the noise is degraded in the input image. In this, the entire region of the image is considered as homogeneous texture. But when the noise content is more, the segmentation of a noisy image into original images become more complex. Hence, Random walker segmentation is implemented for segmentation with canny detection algorithm in order to preserve edges. Then the regions obtained from the segmentation are analyzed using mask NHA algorithm. Theoretical analysis and experimental results are reported to illustrate the usefulness and potential applicability of our algorithm on various computer vision fields, including image enhancement, edge detection, image decomposition, and other applications.
Keywords: Image De-Noising, 2D-NHA, Segmentation, Random Walker, Canny Edge Detection, Mask NHA.
Scope of the Article: Image Security