Single image super-resolution reconstruction using multiple dictionaries and improved iterative back-projection

【摘要】 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.更多还原

【Abstract】 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.更多还原