Wavelet Theory and Application

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). (c) Kluwer Academic Publishers. Manufactured in The Netherlands.

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). (c) Kluwer Academic Publishers. Manufactured in The Netherlands.