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 …

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

Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing
industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of …

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 …

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …

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

Low-light image enhancement aims to recover normal-light images from the images
captured under very dim environments. Existing methods cannot well handle the noise, color …

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision
over the past five years. According to the nature of different applications, designing …

Currently, workers in nuclear power plants need to monitor plant data in real time. In the
event of an emergency, due to human subjectivity, the operator cannot make accurate …

We propose a general dual convolutional neural network (DualCNN) for low-level vision
problems, eg, super-resolution, edge-preserving filtering, deraining, and dehazing. These …