Grahic Jump Location
There has been a great deal of research conducted in the areas of sparse and multiscale data representation, and as a result, numerous approaches and algorithms have been proposed in these areas. Sparse and multiscale representation find their applications in many image and signal processing areas including remote sensing, digital camera applications, biomedical imaging, and data compression. This book presents a summary of recent state-of-the-art work in sparse and multiscale image and signal processing and addresses many of the key perspectives of these areas: analysis and synthesis frameworks for various multiscale approaches (e.g., wavelet, ridgelet, curvelet, etc.), a concise comparison of these multiscale approaches, theoretical insight necessary to understand sparse representation, and the role of sparse and multiscale representation to solve various problems such as denoising, inverse problem regularization, morphological decomposition, and blind source separation.