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• W4372267074 abstract "Blind super-resolution, different from conventional non-blind super-resolution based on the assumption of fixed degradation, handles various unknown Gaussian blur kernels, and thus is closer to real-world application. The accuracy of kernel estimation and deconvolution directly influences the performance of overall super-resolution results, but recent works usually introduce artifacts during the process. In this paper, we propose our methods of a more accurate kernel estimation module (KEM) and deconvolution module (DM). Additionally, KEM and DM are embedded in kernel estimation and deconvolution structure (KEDS), which improves the results to a large extent once combined with non-blind networks." @default.
• W4372267074 created "2023-05-07" @default.
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• W4372267074 date "2023-06-04" @default.
• W4372267074 modified "2023-10-14" @default.
• W4372267074 title "Kernel Estimation and Deconvolution for Blind Image Super-Resolution" @default.
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• W4372267074 doi "https://doi.org/10.1109/icassp49357.2023.10094753" @default.
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