Titlebook: Geometrical Multiresolution Adaptive Transforms; Theory and Applicati Agnieszka Lisowska Book 2014 Springer International Publishing Switze

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書目名稱Geometrical Multiresolution Adaptive Transforms影響因子(影響力)




書目名稱Geometrical Multiresolution Adaptive Transforms影響因子(影響力)學科排名




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書目名稱Geometrical Multiresolution Adaptive Transforms網(wǎng)絡公開度學科排名




書目名稱Geometrical Multiresolution Adaptive Transforms被引頻次




書目名稱Geometrical Multiresolution Adaptive Transforms被引頻次學科排名




書目名稱Geometrical Multiresolution Adaptive Transforms年度引用




書目名稱Geometrical Multiresolution Adaptive Transforms年度引用學科排名




書目名稱Geometrical Multiresolution Adaptive Transforms讀者反饋




書目名稱Geometrical Multiresolution Adaptive Transforms讀者反饋學科排名

DOTE

Multismoothletstly to multiple edges. So, the multismoothlet can adapt to edges of different multiplicity, location, scale, orientation, curvature and blur. Additionally, a notion of sliding multismoothlet was introduced. It is the multismoothlet with location and size defined freely within an image. Based on that

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牽連

Image Compressionespectively. Both methods are based on quadtree decomposition of images. Each description of the compression method was followed by the results of numerical experiments. These results were further compared to the known state-of-the-art methods.

BROOK

Image Denoisingtations are computed for different values of the penalization factor and the optimal approximation is taken as the result. The algorithm description was followed by the results of numerical experiments. These results were further compared to the known state-of-the-art methods. The proposed algorithm

貝雷帽

Edge Detection one is based on sliding multismoothlets. Both methods were compared to the state-of-the-art methods. As follows from the performed experiments, the method based on sliding multismoothlets leads to the best results of edge detection.

貝雷帽

不滿分子

https://doi.org/10.1007/978-3-319-05011-9Edge Detection; Geometrical Methods; Image Compression; Image Denoising; Multiresolution; Multismoothlets

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https://doi.org/10.1007/978-1-4612-2358-0espectively. Both methods are based on quadtree decomposition of images. Each description of the compression method was followed by the results of numerical experiments. These results were further compared to the known state-of-the-art methods.