Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Image denoising is a well explored topic in the field of image processing. In the past several
decades, the progress made in image denoising has benefited from the improved modeling …

Multi-modality image fusion provides more comprehensive and sophisticated information in
modern medical diagnosis, remote sensing, video surveillance, and so on. This paper …

We address the image denoising problem, where zero-mean white and homogeneous
Gaussian additive noise is to be removed from a given image. The approach taken is based …

The limitations of commonly used separable extensions of one-dimensional transforms,
such as the Fourier and wavelet transforms, in capturing the geometry of image edges are …

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 …

In this paper, we develop the nonsubsampled contourlet transform (NSCT) and study its
applications. The construction proposed in this paper is based on a nonsubsampled …

Sparse representations of signals have drawn considerable interest in recent years. The
assumption that natural signals, such as images, admit a sparse decomposition over a …

Sparse coding has received an increasing amount of interest in recent years. It is an
unsupervised learning algorithm, which finds a basis set capturing high-level semantics in …

This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and …