Description: 基于ridgelet变化的图象去噪 含直线特征的效果好-Ridgelet-based image denoising with changes in the characteristics of effective linear Platform: |
Size: 1024 |
Author:xiangzi |
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Description: This paper presents two new 3D curvelet transforms which are built as extensions of the 2D first
generation curvelet transform. The first one, called the RidCurvelet, is especially well designed
for representing 2D surfaces in a 3D space while the second one, the BeamCurvelet, is better
adapted to represent 1D filaments in 3D space. We show that these 3D curvelet transforms can
be built using existing 3D transforms, the 3D wavelet transform, the 3D ridgelet transform,
and the 3D beamlet transform. We illustrate the applicability of these transforms on various
examples such as the detection of linear structures and planar surfaces, as well as on denoising
and inpainting. Platform: |
Size: 1545216 |
Author:Swati |
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Description: 第一代curvelet变换，包括子带分解、平滑分区、重正规化、脊波变换等，再逆变换实现。-The first generation curvelet transform, including the subband decomposition, smooth partition, renormalization, the ridgelet transform and then inverse transform. Platform: |
Size: 1593344 |
Author:肖墨 |
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Description: 超小波分析与应用的讲义，包含2、3、4、7、8、9章内容，有塔式算法、脊波和曲波变换、Surfacelet变换等-The the super wavelet analysis and application of handouts, 2,3,4,7,8,9 chapter, tower algorithm, ridgelet and curvelet transform, Surfacelet transform etc. Platform: |
Size: 11343872 |
Author:刘胡 |
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Description: 曲线波变换是在脊波变换的基础上新产生的一种新型多尺度工具，它能够处理物体沿曲线边缘的特性，它能够进行图像去燥-Curvelet transform is a new multi-scale tool based on ridgelet transform. It is capable of processing, properties of objects along the edge of the curve, it is able to image denoising Platform: |
Size: 12288 |
Author:王瑞 |
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Description: 这本书在稀疏的多尺度图像和信号处理提出了艺术状态，包括线性多尺度变换，如小波，脊波和曲波变换、非线性、多尺度变换基于中值和数学形态学算子。最近的稀疏性和形态多样性的概念描述和利用各种问题，如去噪，反问题正规化，稀疏信号分解，盲源分离，压缩感知。
这本书的理论和实践研究相结合的领域，如天文学、生物学、物理学、数字媒体应用和取证。最后一章探讨了信号处理中的一个范式转换，表明以前的信息取样和提取的限制可以用非常重要的方法加以克服。
MATLAB和IDL代码伴随这些方法和应用程序重现。
实验并说明了在相关网站上可下载的研究的推理和方法。(This book presents the state of the art in sparse and multiscale image and signal processing,
covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms,
and non-linear multiscale transforms based on the median and mathematical
morphology operators. Recent concepts of sparsity and morphological diversity are described
and exploited for various problems such as denoising, inverse problem regularization,
sparse signal decomposition, blind source separation, and compressed sensing.
This book weds theory and practice in examining applications in areas such as astronomy,
biology, physics, digital media, and forensics. A final chapter explores a paradigm
shift in signal processing, showing that previous limits to information sampling and
extraction can be overcome in very significant ways.) Platform: |
Size: 30863360 |
Author:lxfei73 |
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