| 书目名称 | Geometrical Multiresolution Adaptive Transforms |
| 副标题 | Theory and Applicati |
| 编辑 | Agnieszka Lisowska |
| 视频video | |
| 概述 | Presents the recent state-of-the-art of geometrical multiresolution methods leading to sparse image representations.Provides many open problems in the area of geometrical multiresolution methods of im |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | .Modern image processing techniques are based on multiresolution geometrical methods of image representation. These methods are efficient in sparse approximation of digital images. There is a wide family of functions called simply ‘X-lets’, and these methods can be divided into two groups: the adaptive and the nonadaptive. This book is devoted to the adaptive methods of image approximation, especially to multismoothlets..Besides multismoothlets, several other new ideas are also covered. Current literature considers the black and white images with smooth horizon function as the model for sparse approximation but here, the class of blurred multihorizon is introduced, which is then used in the approximation of images with multiedges. Additionally, the semi-anisotropic model of multiedge representation, the introduction of the shift invariant multismoothlet transform and sliding multismoothlets are also covered..Geometrical Multiresolution Adaptive Transforms. should be accessible to both mathematicians and computer scientists. It is suitable as a professional reference for students, researchers and engineers, containing many open problems and will be an excellent starting point for th |
| 出版日期 | Book 2014 |
| 关键词 | Edge Detection; Geometrical Methods; Image Compression; Image Denoising; Multiresolution; Multismoothlets |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-05011-9 |
| isbn_softcover | 978-3-319-37714-8 |
| isbn_ebook | 978-3-319-05011-9Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer International Publishing Switzerland 2014 |
发表于 2025-3-21 16:40:02
