A NOVEL CONVOLUTIONAL AUTOENCODERS BASED MULTILEVEL DENOISING FRAMEWORK FOR MEDICAL IMAGE DATA

Abstract

Convolutional autoencoders based image denoising has emerged as a promising approach in medical imaging applications. Most of the traditional denoising methods such as Bayesian filter, non-linear median filter, wavelet based shearlet transform etc, are difficult to process medical images due to the existence of additive, multiplicative and Gaussian noise. Also, these models cannot resolve the issue of sparsity in the compressed medical images. To overcome these issues, a novelcompressive sensing based multilevel denoising technique is proposed to minimize the noise rate of medical images in the Convolution neural network(CNN) framework. This technique is implemented in convolutional autoencoders framework to reduce the noise in each deep network layer and to improve the quality of the images. This technique efficiently denoise the medical images using the inter, intra variance in the CNN framework. Experimental results demonstrated that the proposed framework has better PSNR and SSIM measures compared to the traditional state-of-the-art approaches.