High Density Noise Reduction of Tea Leaves using Density Mass Filter (DMF)
P. Velmurugan1, M. Renuka Devi2

1Velmurugan P, Research Scholar, Part Time Ph.D, Bharathiar University, Coimbatore.
2Dr. M. Renuka Devi, Professor & Head, Department of BCA, Sri Krishna Arts and Science College, Coimbatore.

Manuscript received on 29 August 2019. | Revised Manuscript received on 08 September 2019. | Manuscript published on 30 September 2019. | PP: 2988-2991 | Volume-8 Issue-11, September 2019. | Retrieval Number: K22950981119/2019©BEIESP | DOI: 10.35940/ijitee.K2295.0981119
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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 agriculture digital image processing play an important role in the prediction of tea leaves diseases. But acquisition of image may be corrupted by various types of noise such as impulse noise, Gaussian noise and salt and pepper noise. These noises can corrupt the image. So it will reduce the quality of the image and it reduces the classification accuracy. Hence it needs a efficient filter to remove these noise. This paper introduced a new filter density mass filter. It reduces all kinds of noise. Two metrics PSNR (Peak Signal to Noise ratio) and RMSE (Root Mean Square Error) values are used to evaluate the quality of images. The PSNR value of proposed filter is significantly high and RMSE value is reasonably low.
Keywords: PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error), Digital Image Processing.
Scope of the Article: Signal and Image Processing