Modeling and Performance Evaluation of Generative adversarial network for image denoising
V NUTHNA, Kavita Chachadi, Leah Joshi · 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) · 2018
During transmission and reception the images are degraded by noise. Also Images captured using different low quality devices adversely affect the visual quality of images. The presence of the noise results in loss of visibility, gives a mottled, grainy, textured, or snowy appearance. Thus making the image visually unpleasing which drastically affects the human vision. In addition during image processing, presence of noise makes it hard for further processing (impulse, Gaussian white, salt and pepper, adversarial etc.,). Existing methods use conventional filters and Neural network models for image denoising where they compromise with the visibility of image after rigorous iterations of denoising algorithms. In this paper we implement CGANs for image denoising and evaluate the performance of CGAN with different Neural network models viz., CNN ,GAN for single or multiple image denoising problem. The qualitative performance of de-noised image/images is measured using PSNR and confusion matrix.