Implementing Various Noise Reduction Filters On Infected Coconut Images
Sangeetha Muthiah1, A. Senthilrajan2
1S.Dhanasekar*, Vellore Institute of Technology , Chennai Campus, Chennai (Tamil Nadu), India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 3675-3678 | Volume-8 Issue-12, October 2019. | Retrieval Number: L3811081219/2019©BEIESP | DOI: 10.35940/ijitee.L3811.1081219
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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: In recent times, more people are returning to farming. The inexperienced farmers facing problems in getting high crop yield. One of the challenges is to control crop diseases and pests. The farmers are not aware of the Climate change-induced agricultural diseases and pests. The image processing techniques have an important part in recognizing the diseases and pests infestations in the agricultural crop. The image must be free of noise for effective diagnosis. This paper analyses the performance of various noises and different de-noising techniques on an infected coconut image. In this paper, a suitable de-noising technique to remove various kinds of noise in an image. The performance of the filters is compared for different types of noises and the quality is measured based on PSNR and MSE.
Keywords: De-noising, Gaussian filter, MSE, PSNR
Scope of the Article: Image Processing and Pattern Recognition