Sparse image and signal processing
代码说明：
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.)
文件列表：
Starck J.L., Murtagh F., Fadili J. Sparse image and signal processing Wavelets, curvelets, morphological diversity.pdf, 31565854 , 2017-08-26
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