Wavelet Theory and Application : A Special Issue of the Journal of Mathematical Imaging and Vision

Finally, Moulin considers the problem of forming radar images under a diffuse-target statistical model. His estimation approach includes application of the maximum-likelihood principle and a regularization procedure based on wavelet representations. In addition, he shows that the radar imaging problem can be seen as a problem of inference on the wavelet coefficients of an image corrupted by additive noise. The aim of this special issue is to provide a forum in which researchers from the fields of mathematics, computer science, and electrical engineering who work on problems of significance to computer vision can better understand each other. I hope that the papers included in this special issue will provide a clearer picture of the role of wavelet transforms and the principles of multiresolution analysis. I wish to thank many people for their contributions and assistance in this project: Gerhard Ritter, the Editor-in-Chief of the Journal of Mathematical Imaging and Vision, who invited me to organize this issue and who provided patient guidance; the researchers who submitted papers for consideration and others who have contributed to the explosion of growth in this area; the reviewers, who provided careful and thoughtful evaluations in a timely fashion; and, finally, from these efforts, the authors of the papers selected for publication in the special issue. Andrew Laine Guest Editor Center for Computer Vision and Visualization Department of Computer and Information Sciences University of Florida Journal of Mathematical Imaging and Vision, 3, 7-38 (1993). © Kluwer Academic Publishers. Manufactured in The Netherlands. The development of wavelet transform has provided a wealth of new mathematical results and has made possible a common ground for researchers working in a wide variety of fields, including harmonic analysis,
mathematical physics, digital signal processing, image processing, and computer vision. This book presents a wide-ranging collection of perspectives on wavelets and multiresolution signal analysis by authors from a variety of disciplines. The analysis of signals and phenomena at multiple scales of resolution remains an evolving science. Thus the contributions in this book provide a snapshot of a maturing subject, where the principles of multiresolution analysis and the contributions of wavelet transforms are further distilled. The seven chapters included in this book are grouped into three general areas: (1) image compression techniques, (2) mathematical models and formulation, and (3) applications in medical imaging and targe recognition. Wavelet Theory and Application is an edited volume of original research. Front Matter....Pages 1-3 Introduction....Pages 5-6 Local Cosine Transform — A Method for the Reduction of the Blocking Effect in JPEG....Pages 7-38 Local Enhancement of Compressed Images....Pages 39-49 Image Analysis by Wavelet-Type Transforms: Group Theoretic Approach....Pages 51-77 Two Ways to Incorporate Scale in the Heisenberg Group with an Intertwining Operator....Pages 79-94 The Generalized Matrix Product and the Wavelet Transform....Pages 95-104 Construction of Wavelet Decompositions for Tomographic Images....Pages 105-122 A Wavelet Regularization Method for Diffuse Radar-Target Imaging and Speckle-Noise Reduction....Pages 123-134 Back Matter....Pages 135-135