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- W2360435117 abstract "In this paper a novel method for image superresolution is proposed. The primary theory is proved that the 2 dimension image data can be approximated by the products of 1 dimension functions whose variables are separated from the image's variables. Therefore, the image superresolution can proceed conveniently through the 1 dimension superresolution. Concretely, the digital image ( M×N ) can be expressed by the summation of the products of M dimension vectors and N dimension vectors. So the image superresolution process can be converted to the M dimension vector processing and the N dimension vector processing easily. Thus the method is based on the eigenvectors. In the mean square error sense, this expression or decomposition is optimum. It is also proved to be identical with the literature[3] when the hits go to infinite. At last the applications verify the theoretical result. Namely, this method has the better results and can reduce the calculations because the image can be adjusted adaptively and be expressed by the less parameters. In addition, this method can also be applied to other fields of image processing and the information processing of the great capacity data." @default.
- W2360435117 created "2016-06-24" @default.
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- W2360435117 date "2004-01-01" @default.
- W2360435117 modified "2023-09-25" @default.
- W2360435117 title "Image Superresolution Basing on the Optimum Discomposition" @default.
- W2360435117 hasPublicationYear "2004" @default.
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