Independent component analysis and nongaussianity for blind image deconvolution and deblurring
نویسندگان
چکیده
منابع مشابه
Independent component analysis and nongaussianity for blind image deconvolution and deblurring
Blind deconvolution or deblurring is a challenging problem in many signal processing applications as signals and images often suffer from blurring or point spreading with unknown blurring kernels or point-spread functions as well as noise corruption. Most existing methods require certain knowledge about both the signal and the kernel and their performance depends on the amount of prior informat...
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Blind deconvolution problems arise in image analysis when both the extent of image blur, and the true image, are unknown. If a model is available for at least one of these quantities then, in theory, the problem is solvable. It is generally not solvable if neither the image nor the point-spread function, which controls the extent of blur, is known parametrically. In this paper we develop method...
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In this paper we study the applicability of classical blind deconvolution methods such as constant modulus algorithm (CMA) for blind adaptive image restoration. The requirements such as the source to be white, uniformly distributed and zero mean, which yield satisfactory convergence in the data communication application context, are revisited in the image restoration context, where a linear deb...
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Independent Component Analysis is a new data analysis method, and its computation algorithms and applications are widely studied recently. Most applications, however, are for the field of one-dimensional data analysis, e.g. sound data analysis, and few applications for two-dimensional data (e.g., image data) are studied. In this paper we give a new blind deconvolution algorithm for images. In o...
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ژورنال
عنوان ژورنال: Integrated Computer-Aided Engineering
سال: 2008
ISSN: 1875-8835,1069-2509
DOI: 10.3233/ica-2008-15302