Deep learning has been widely applied in many computer vision tasks due to its impressive capability of automatic feature extraction and classification. Recently, deep neural networks have been used in image denosing, but most of the proposed approaches were designed for Gaussian noise suppression. Therefore, in this paper, we address the problem of impulsive noise reduction in color images using Denoising Convolutional Neural Networks (DnCNN). This network architecture utilizes the concept of deep residual learning and is trained to learn the residual image instead of the directly denoised one. Our preliminary results show that direct application of DnCNN allows to achieve significantly better results than the state-of-the-art filters designed for impulsive noise in color images.
A novel fast filtering technique for multiplicative noise removal in ultrasound images was presented in this paper. The proposed algorithm utilizes concept of digital paths created on the image grid presented in  adapted to the needs of multiplicative noise reduction. The new approach uses special type of digital paths so called <i>Escaping</i> <i>Path</i> <i>Model</i> and modified path length calculation based on topological as well as gray-scale distances. The experiments confirmed that the proposed algorithm achieves a comparable results with the existing state-of-the-art denoising schemes in suppressing multiplicative noise in ultrasound images.