As a result of several successful applications in computer vision and image processing,
sparse representation (SR) has attracted significant attention in multi-sensor image fusion …

Image denoising is a well explored topic in the field of image processing. In the past several
decades, the progress made in image denoising has benefited from the improved modeling …

Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

Deep convolutional neural networks perform better on images containing spatially invariant
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …

Invertible networks have various benefits for image denoising since they are lightweight,
information-lossless, and memory-saving during back-propagation. However, applying …

Depth image denoising is increasingly becoming the hot research topic nowadays, because
it reflects the three-dimensional scene and can be applied in various fields of computer …

Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

Deep neural networks (DNNs) have shown very promising results for various image
restoration (IR) tasks. However, the design of network architectures remains a major …

Image restoration is a challenging ill-posed problem which also has been a long-standing
issue. In the past few years, the convolution neural networks (CNNs) almost dominated the …

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