Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …

Deep convolutional neural networks (CNNs) are used for image denoising via automatically
mining accurate structure information. However, most of existing CNNs depend on enlarging …

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard
and important tasks for several applications in the field of medical analysis. As each brain …

As the computing power of modern hardware is increasing strongly, pre-trained deep
learning models (eg, BERT, GPT-3) learned on large-scale datasets have shown their …

Due to strong learning ability, convolutional neural networks (CNNs) have been developed
in image denoising. However, convolutional operations may change original distributions of …

Recently, deep learned enabled end-to-end communication systems have been developed
to merge all physical layer blocks in the traditional communication systems, which make joint …

The rapid development of deep learning (DL) and widespread applications of Internet-of-
Things (IoT) have made the devices smarter than before, and enabled them to perform more …

The training of a feature extraction network typically requires abundant manually annotated
training samples, making this a time-consuming and costly process. Accordingly, we …

In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous
works, we propose to tackle this challenging problem from a new perspective: noise …