Description: Denoising source separation is a new source separation framework proposed by Jaakko Sä relä and Harri Valpola. It is based on denoising procedures.
For more information, see the comprehensive journal paper Denoising source separation published in Journal of Machine Learning Research and the Web tutorials
-Denoising source separation is a new source separation framework proposed by Jaakko S盲 rel盲 and Harri Valpola. It is based on denoising procedures. For more information, see the comprehensive journal paper Denoising source separation published in Journal of Machine Learning Research and the Web tutorials Platform: |
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Author:roz yang |
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Description: 盲源分离算法的几篇文章应用
【基于独立分量分析的脑电中眼电伪迹消除】【基于负熵和智能优化的盲源分离方法】【基于小波消噪和盲源分离的信号奇异点检测方法】-Blind source separation algorithm applied 【several articles based on independent component analysis of EEG ocular artifact】 【intelligent optimization based on negative entropy and Blind Source Separation Method】 【based on wavelet denoising and blind source separation of signal singularity Detection】 Platform: |
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Author:Hekate |
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Description: 基于盲源分离的地震资料去噪技术，对于做地震资料处理的人有帮助-Blind source separation based on seismic data denoising technique for processing seismic data do help people Platform: |
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Author:王佳佳 |
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Description: 基于新型阈值处理函数的多算法融合小波去噪方法，去燥效果较好，可实现一维信号的有效降噪！另附有小波盲源分离代码-A Better Method based on a novel fusion algorithm of multi-threshold processing function , can achieve the effective denoising for one-dimensional signal. Additionally,Wavelet Blind Source Separation code is attached. Platform: |
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Author:libo |
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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: |
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Author:lxfei73 |
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