Automatic Noise Detection and Reduction in Images
Manjunath P C1, Dhanush.S2, G.Uday Teja3, G.Srinidhi4, N.Madhu Babu5
1Manjunath P C, REVA University, India.
2Dhanush.S, REVA University, India.
3G.UdayTeja, REVA University, India.
4G.Srinidhi, REVA University, India.
5N.MadhuBabu, REVA University, India
Manuscript received on 04 June 2019 | Revised Manuscript received on 12 June 2019 | Manuscript Published on 29 June 2019 | PP: 121-124 | Volume-8 Issue-5S, May 2019 | Retrieval Number: E10250585S19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Data classification in presence of noise will cause a lot of worse results than expected for pure patterns. In the proposed work we tend to investigate the drawback within the case of deep convolutional neural networks so as to propose solutions which will mitigate influence of noise. The main contributions presented in this proposed work include using convolution neural network as an image classifier for detecting noise in the images and using different opencv2 inbuilt methods to mitigate noise in the images. Though a number of techniques are introduced for this purpose, using neural networks we can achieve a greater accuracy.
Keywords: Convolution Neural Networks, Open cv2, Keras API, Jupyter Notebook, TensorFlow.
Scope of the Article: Image Security