Efficient Denoising by Feature Recovery from Residual Noise in Spatial Domain
Amina Girdher1, Bhawna Goyal2, Ayush Dogra3, Anaahat Dhindsa4, Sumit Budhiraja5
1Amina Girdher, Pursuing Doctor of Philosophy, Engineering, IIT Jammu.
2Bhawna Goyal, Doctor of Philosophy, Engineering, Panjab University.
3Ayush Dogra, CSIR-NPDF, CSIR-CSIO lab, Chandigarh.
4Anaahat Dhindsa, Assistant Professor, University Institute of Engineering and Technology, PU Chandigarh.
5Sumit Budhiraja, Assistant Professor, University Institute of Engineering and Technology, Panjab University, Chandigarh.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 947-957 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7604118419/2019©BEIESP | DOI: 10.35940/ijrte.D7604.118419

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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 growth of image processing techniques has lead to limitless applications of imaging in various fields of medical, remote sensing. However, the unpreventable problem of noise contamination arises during the processing of images. As the noise content in an image rises, it becomes difficult to denoise an image while preserving high-frequency edge features as well as low frequency smooth features. Minimal artefacts and better preservation of geometrical details such as edges and texture reflects efficient image reconstruction. Since many State-of-the-art denoising algorithms have been reported in the literature, and there is always a compromise between noise removal and feature preservation. A novel approach for efficient noise removal along with recovery of fine features is being proposed. The idea behind denoising approach is the use of hybridization of spatial domain filters where base layer image and residual image are extracted and processed separately to mitigate the prevalence of artefacts and preserve image content. The performance of the proposed method is evaluated both quantitatively as well as qualitatively, and it is found that the proposed method could outperform existing denoising techniques.
Keywords: Image Noise Cancellation, Image Denoising, Nonlinear Diffusion, Texture, Spatial Filters, Transforms.
Scope of the Article: Microwave Filter.