Multimodal medical image fusion consists in combining two or more images of the same or
different modalities aiming to improve the image content, and preserve information. The …

The medical image fusion is the process of coalescing multiple images from multiple
imaging modalities to obtain a fused image with a large amount of information for increasing …

Regularization methods are a key tool in the solution of inverse problems. They are used to
introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses …

During the late 1990s, Huang introduced the algorithm called Empirical Mode
Decomposition, which is widely used today to recursively decompose a signal into different …

We give a methodology-oriented perspective on directional image analysis and rotation-
invariant processing. We review the state of the art in the field and make connections with …

In the last two decades, significant research has been conducted in the field of automated
extraction of rock mass discontinuity characteristics from three-dimensional (3D) models …

Sparse and redundant representation modeling of data assumes an ability to describe
signals as linear combinations of a few atoms from a pre-specified dictionary. As such, the …

This paper describes two digital implementations of a new mathematical transform, namely,
the second generation curvelet transform in two and three dimensions. The first digital …

In spite of their remarkable success in signal processing applications, it is now widely
acknowledged that traditional wavelets are not very effective in dealing multidimensional …

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 …