Bio-medical Image Denoising using Autoencoders

Abstract

Image denoising, in which the original image is reconstructed by eliminating noise from a noise-corrupted version of the original image, is one of the greatest requirements in the image processing field. The sources of noise in image may be in-trinsic (such as sensors) and extrinsic (such as environment) that are often practically unavoidable. One of the crucial initial steps in biomedical image processing and analysis is image denoising as improved visual quality of images may increase the accuracy of medical diagnosis and image preprocessing is required at the input for analytical methods like segmentation and content recognition. Autoencoder is an artificial neural network that is trained in the unsupervised manner to reconstruct its input at the output. This work aims at the development of a denoising convolutional autoencoder which will first generate the encoded lower-dimensional representation of the image and then decodes the image back from the lower-dimensional representation. The denoising autoencoders build corrupted copies of the input images by adding random noise and then attempt to eliminate the noise from the noisy input to reconstruct the output that is very similar to the original input. Medical images like X-Ray, Magnetic Resonance Imaging(MRI) etc. which are corrupted by various types of noise like Gaussian noise, Rician noise etc. are denoised using the convolutional denoising autoencoder and their denoising efficiency is measured using the Structural Similarity Index Measure(SSIM) parameter of both the noisy image and the denoised image compared to the original noise free image and the Peak Signal to Noise Ratio(PSNR) of the denoised image with the noise free image as reference.