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) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

Deep convolutional neural networks (CNNs) have attracted considerable interest in low-
level computer vision. Researches are usually devoted to improving the performance via …

Deep convolutional neural networks (CNNs) with strong learning abilities have been used in
the field of image denoising. However, some CNNs depend on a single deep network to …

The aim of this review is to provide a broad overview of the state-of-the-art works mainly
published in the last ten years on expert systems applied in different medical domains …

In this paper, we propose a general framework for incomplete multiview clustering. The
proposed method is the first work that exploits the graph learning and spectral clustering …

Deep convolutional neural networks (CNNs) for image denoising have recently attracted
increasing research interest. However, plain networks cannot recover fine details for a …

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved
impressive performances on single image super-resolution (SISR). However, their excessive …