Block-adaptive DCT-Wiener image up-sampling

DCT-Wiener image up-sampling scheme is highly desirable since it makes use of the advantages of the information in both spatial and DCT domains. The idea is to combine the observed low-frequency DCT coefficients with the estimated high-frequency DCT coefficients obtained by the Wiener filters in the spatial domain. However, the available 1-D and 2-D Wiener filters that were proposed for high-frequency DCT coefficients estimation are block non-adaptive, mainly due to the limited information from the observed image. In this paper, we propose a block-adaptive Wiener filter by utilizing the information from external training data. During the online estimation, for each image block, the k-nearest relevant DCT LR-HR block pairs are searched from the training data, in order to estimate the coefficients of the Wiener filter. Experimental results show that the proposed block-adaptive Wiener filter improves the PSNR value of the DCT-Wiener scheme by 1.5 dB compared with that using non-adaptive 1-D Wiener filter.