A full-rank matrix \bfA∈R^n*m with n<m generates an underdetermined system of linear
equations \bfAx=\bfb having infinitely many solutions. Suppose we seek the sparsest …

Wavelet methods have become a widely spread tool in signal and image process ing tasks.
This book deals with statistical applications, especially wavelet based smoothing. The …

Shrinkage is a well known and appealing denoising technique, introduced originally by
Donoho and Johnstone in 1994. The use of shrinkage for denoising is known to be optimal …

This paper proposes an adaptive threshold estimation method for image denoising in the
wavelet domain based on the generalized Guassian distribution (GGD) modeling of …

We propose a vector/matrix extension of our denoising algorithm initially developed for
grayscale images, in order to efficiently process multichannel (eg, color) images. This work …

Digital images always inherit some extent of noise in them. This noise affects the information
content of the image. Removal of this noise is very important to extract useful information …

Multiview clustering has wide real-world applications because it can process data from
multiple sources. However, these data often contain missing instances and noises, which …

Advances in sensors, information technology, and high-performance computing have
resulted in massive data sets becoming available in many scientific disciplines. These data …

Unlike the maturity of image denoising research, video denoising has remained a
challenging problem. A fundamental issue at the core of the video denoising (VD) problem is …

Image denoising has remained a fundamental problem in the field of image processing. With
Wavelet transforms, various algorithms for denoising in wavelet domain were introduced …