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 …

In this paper, we propose a state-of-the-art video denoising algorithm based on a
convolutional neural network architecture. Until recently, video denoising with neural …

In this paper, we propose a state-of-the-art video denoising algorithm based on a
convolutional neural network architecture. Previous neural network based approaches to …

Denoising is a common preprocessing step prior to the analysis and interpretation of
hyperspectral images (HSIs). However, the vast majority of methods typically adopted for …

Random noise attenuation has always been an indispensable step in the seismic
exploration workflow. The quality of the results directly affects the results of subsequent …

Seismic data denoising has always been an indispensable step in the seismic exploration
workflow. The quality of the results directly affects the results of subsequent inversion and …

As training Deep Neural Networks (DNNs) becomes more expensive, the interest in
protecting the ownership of the models with watermarking techniques increases. Uchida et …

The challenge of performing relative radiometric correction on raw nighttime-light (NTL)
images captured by the Glimmer Image for Urbanization (GIU) sensor of the SDGSAT-1 …

The goal of this paper is to find whether a convolutional neural network (CNN) performs
better than the existing blind algorithms for image denoising, and, if yes, whether the noise …

Noise appears in images captured by real cameras. This paper studies the influence of
noise on monocular feature-based visual Simultaneous Localization and Mapping (SLAM) …