Open AccessConvolution Neural Network based Rain Noise Removal for Real Time Application
Author(s) -
A. Mary Sowjanya,
Sandeep Kumar,
K. Sonali Swaroop
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1042/1/012001
Subject(s) - computer science , convolutional neural network , mist , noise (video) , convolution (computer science) , path (computing) , process (computing) , streak , artificial intelligence , image (mathematics) , artificial neural network , data mining , computer vision , pattern recognition (psychology) , meteorology , geography , geology , programming language , operating system , mineralogy
Image quality is affected by rain streaks that have an adverse effect on the applications which involves the ideas of surveillance, automatic path navigation. Hence it is important to process the information present in the images affected by water droplets caused by the rain or the mist. So this forms the basic need to identify the problem which does not have any mathematical correlation about the same. This research tries to work on the methodology which will denoise the criteria based on Convolutional neural network (CNN). CNN modelling helps in finding out the connections between the noise input image and rain streak. The proposed method uses synthetic database available publicly.