This paper is a brief survey of advance technological aspects of Digital Image Processing
which are applied to remote sensing images obtained from various satellite sensors. In …

Hyperspectral imaging, providing abundant spatial and spectral information simultaneously,
has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations …

Recent low-rank based matrix/tensor recovery methods have been widely explored in
multispectral images (MSI) denoising. These methods, however, ignore the difference of the …

Low-rank matrix approximation (LRMA)-based methods have made a great success for
grayscale image processing. When handling color images, LRMA either restores each color …

The rapidly increasing number of images on the internet has further increased the need for
efficient indexing for digital image searching of large databases. The design of a cloud …

Filtering images of more than one channel are challenging in terms of both efficiency and
effectiveness. By grouping similar patches to utilize the self-similarity and sparse linear …

There is a recurrent idea being promoted in the recent literature on iterative solvers for
imaging problems, the idea being the use of an actual denoising step in each iteration. We …

Supervised learning with a convolutional neural network is recognized as a powerful means
of image restoration. However, most such methods have been designed for application to …

Due to the physical nature of color images, color image processing such as denoising and
inpainting has shown extensive and versatile possibilities over grayscale image processing …

Hyperspectral images (HSIs) denoising aims at eliminating the noise generated during the
acquisition and transmission of HSIs. Since denoising is an ill-posed problem, utilizing …