In order to improve the super-resolution reconstruction effect of the single image, a novel multiple dictionaries learning via support vector regression(SVR) and improved iterative back-projection(IBP) are proposed.To...In order to improve the super-resolution reconstruction effect of the single image, a novel multiple dictionaries learning via support vector regression(SVR) and improved iterative back-projection(IBP) are proposed.To characterize the image structure, the low-frequency dictionary is constructed from the normalized brightness of low-frequency image patches in a discrete-cosine-transform(DCT) domain.Pixels determined by Gaussian weighting are added to the input vector to restore more high-frequency information when training the high-frequency image patch dictionary in the space domain.During post-processing, the improved IBP is employed to reduce regression errors each time.Experiment results show that the peak signal-to-noise ratio(PSNR)and structural similarity(SSIM) of the proposed method are enhanced by 1.6%—5.5% and 1.5%—13.1% compared with those of bicubic interpolation, and the proposed method visually outperforms several algorithms.展开更多
基金supported by the Tianjin Applied Basic and Frontier Technology Research Program of Youth Fund Funding Project(No.14JCQNJC00900)the Tianjin Education Commission Project(No.2018kj132)
文摘In order to improve the super-resolution reconstruction effect of the single image, a novel multiple dictionaries learning via support vector regression(SVR) and improved iterative back-projection(IBP) are proposed.To characterize the image structure, the low-frequency dictionary is constructed from the normalized brightness of low-frequency image patches in a discrete-cosine-transform(DCT) domain.Pixels determined by Gaussian weighting are added to the input vector to restore more high-frequency information when training the high-frequency image patch dictionary in the space domain.During post-processing, the improved IBP is employed to reduce regression errors each time.Experiment results show that the peak signal-to-noise ratio(PSNR)and structural similarity(SSIM) of the proposed method are enhanced by 1.6%—5.5% and 1.5%—13.1% compared with those of bicubic interpolation, and the proposed method visually outperforms several algorithms.
