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

• 来源期刊: 光电子快报（英文版）
• 年，卷（期）: 2019,（2）
• 页码范围: 156-160
• 页数: 5页
• 语种: 英文
• DOI: 10.1007/s11801-019-8138-x

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.