| 書目名稱 | Minimax Theory of Image Reconstruction |
| 編輯 | A. P. Korostelev,A. B. Tsybakov |
| 視頻video | http://file.papertrans.cn/635/634612/634612.mp4 |
| 叢書名稱 | Lecture Notes in Statistics |
| 圖書封面 |  |
| 描述 | There exists a large variety of image reconstruction methods proposed by different authors (see e. g. Pratt (1978), Rosenfeld and Kak (1982), Marr (1982)). Selection of an appropriate method for a specific problem in image analysis has been always considered as an art. How to find the image reconstruction method which is optimal in some sense? In this book we give an answer to this question using the asymptotic minimax approach in the spirit of Ibragimov and Khasminskii (1980a,b, 1981, 1982), Bretagnolle and Huber (1979), Stone (1980, 1982). We assume that the image belongs to a certain functional class and we find the image estimators that achieve the best order of accuracy for the worst images in the class. This concept of optimality is rather rough since only the order of accuracy is optimized. However, it is useful for comparing various image reconstruction methods. For example, we show that some popular methods such as simple linewise processing and linear estimation are not optimal for images with sharp edges. Note that discontinuity of images is an important specific feature appearing in most practical situations where one has to distinguish between the "image domain" and th |
| 出版日期 | Book 1993 |
| 關(guān)鍵詞 | estimator; image analysis; likelihood; statistical model |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-1-4612-2712-0 |
| isbn_softcover | 978-0-387-94028-1 |
| isbn_ebook | 978-1-4612-2712-0Series ISSN 0930-0325 Series E-ISSN 2197-7186 |
| issn_series | 0930-0325 |
| copyright | Springer-Verlag New York, Inc. 1993 |
|Archiver|手機版|小黑屋|
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