Deep convolutional neural networks (CNNs) have attracted great attention in the field of
image denoising. However, there are two drawbacks:(1) it is very difficult to train a deeper …

This letter introduces a hyperspectral denoising algorithm based on spatio-spectral total
variation. The denoising problem has been formulated as a mixed noise reduction problem …

Ground-based whole-sky cameras have opened up new opportunities for monitoring the
earth's atmosphere. These cameras are an important complement to satellite images by …

Remote sensing image fusion is consistently used to turn raw images of different resolutions,
sources, and modalities into accurate, complete, and spatiotemporally coherent images. It …

The current literature documents a plethora of image denoising techniques in the fields of
medical imaging, remote sensing, biometrics, surveillance and vegetation map** …

To meet the needs of urban public and the city development smartly, the use of IoT devices,
such as sensors, actuators, and smartphones, etc., and the smart system is the very fast and …

Sparse representation of real-life images is a very effective approach in imaging
applications, such as denoising. In recent years, with the growth of computing power, data …

Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve
the quality of images for object detection, classification, and other subsequent applications. It …

In this paper, we propose a hyperspectral image denoising algorithm with a Spatial-spectral
view fusion strategy. The idea is to denoise a noisy hyperspectral 3-D cube using the …

Satellite image denoising is essential for enhancing the visual quality of images and for
facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite …