Sharif Digital Repository

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performance of deep neural networks in different applications of image processing and computer vision, we present an end-to-end fully convolutional neural network to remove impulsive noise from images. To train our network, we generate a customized dataset with various noise densities in which the highly corrupted images are more frequent. Hence, our convolutional neural network is blind since the percentage of impulsive noise is not required as... 

In this letter, we consider the problem of blind source separation under certain nonlinear mixing conditions using a deep learning approach. Conventionally, the separation of sources within linear mixtures is achieved by applying the independence property of the sources. In the nonlinear regime, however, this property is no longer sufficient. In this letter, we consider nonlinear mixing operators where the non-linearity could be fairly approximated using a Taylor series. Next, for solving the nonlinear BSS problem, we design an end-to-end recurrent neural network (RNN) that learns the inverse of the system, and ultimately separates the sources. For training the RNN, we employ a set of... 

Prostate Cancer has become one of the deadliest cancers among males in many nations. Pathologists use various approaches for the detection and the staging of prostate cancer. Microscopic inspection of biopsy tissues is the most accurate approach among them. The Gleason grading system is used to evaluate the stage of Prostate Cancer using prostate biopsy samples. The task of assigning a grade to each region in a tissue is a time-consuming task. Furthermore, this task often has several challenges since it has considerable inter-observer variability even among expert pathologists. In this paper, we propose an automatic method for this task using a deep learningbased approach. For this purpose,...