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 …

Consistency and realness have always been the two critical issues of image super-
resolution. While the realness has been dramatically improved with the use of GAN prior, the …

Contemporary deep learning based semantic inpainting can be approached from two
directions. First, and the more explored, approach is to train an offline deep regression …

In real-world environments, ranging from urban disastrous scenes to underground mining
tunnels, autonomous mobile robots are being deployed in harsh and cluttered …

In recent years, methods founded on deep learning have exhibited notable efficacy within
the field of medical image denoising. However, the majority of deep learning approaches …

We propose a new image denoising algorithm, dubbed as Fully Convolutional Adaptive
Image DEnoiser (FC-AIDE), that can learn from an offline supervised training set with a fully …

Magnetic resonance imaging (MRI) images acquired as multislice two-dimensional (2D)
images present challenges when reformatted in orthogonal planes due to sparser sampling …

Micro-computed tomography (CT) is an irreplaceable tool to characterize the three-
dimensional microstructure of fiber-reinforced composites and other fibrous materials …

We tackle a challenging blind image denoising problem, in which only single distinct noisy
images are available for training a denoiser, and no information about noise is known …

We tackle a challenging blind image denoising problem, in which only single noisy images
are available for training a denoiser and no information about noise is known, except for it …