Convolutional Autoencoder for Image Denoising - Ghafar, Abdul; Sattar, Usman (UMT Artificial Intelligence Review, 2021)
Image denoising is a process used to remove noise from the image to create a sharp and clear image. It is mainly used in medical imaging, where due to the malfunctioning of machines or due to the precautions taken to protect patients from radiation, medical imaging machines create a lot of noise in the final image. Several techniques can be used in order to avoid such distortions in the image before their final printing. Autoencoders are the most notable software used to denoise images before their final printing. These software are not intelligent so the resultant image is not of good quality. In this paper, we introduced a modified autoencoder having a deep convolutional neural network. It creates better quality images as compared to traditional autoencoders. After training with a test dataset on the tensor board, the modified autoencoder is tested on a different dataset having various shapes. The results were satisfactory but not desirable due to several reasons. Nevertheless, our proposed system still performed better than traditional autoencoders. KEYWORDS: image denoising, deep learning, convolutional neural network, image autoencoder, image convolutional autoencoder