Application of FFDNET for Image Denoising On Microarray Images
Priya Nandihal1, Vandana Sreenivas2, Jagadeesh Pujari3
1Priya Nandihal*,ISE department, SDMCET, Dharwad, India.
2Vandana S Bhat, ISE department, SDMCET, Dharwad, India.
3Jagadeesh Pujari , ISE department, SDMCET, Dharwad, India.
Manuscript received on 5 August 2019. | Revised Manuscript received on 11 August 2019. | Manuscript published on 30 September 2019. | PP: 2691-2694| Volume-8 Issue-3 September 2019 | Retrieval Number: C4950098319/2019©BEIESP | DOI: 10.35940/ijrte.C4950.098319
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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: Microarray technology allows the simultaneous profiling of thousands of genes. Denoising is an important pre-processing step in microarray image analysis for accurate gene expression profiling. In this paper, as FFDNet provides model independent denoising technique, it is been applied on microarray images. FFDNet is validated on AWGN based images and real noisy images trained network. The application is compared with the standard denoising methods. The results revealed that optimal sigma value to efficiently remove noise while preserving details for AWGN based images and real noisy trained methods were 15 and 20 respectively. Overall, the performance of the FFDNet is better compared to other metrics considered in the study as it is flexible, effective and fast. It is also capable to maintain the trade-off between denoising and feature preservation.
Keywords: Microaray, Denoising, FFDNET, AWGN, Sigma Value, Performance Metrics..
Scope of the Article: High Performance Computing