Detail enhancement of image super-resolution based on detail synthesis

In this paper, a detail-enhancement and super-resolution algorithm based on detail synthesis is proposed. The novelty of this algorithm is in combining local self-similarity search and singular value decomposition of patches together to obtain details with more natural high-frequency. The proposed algorithm improves the facet or line phenomenon on edges and areas that have rich texture. The algorithm firstly searches for an image patch and extracts the high-frequency components based on a local self-similarity of the original, low-resolution image. The matrix of the high-frequency block is then decomposed into two sub-spaces by the singular value decomposition and the pseudo high-frequency is removed by a soft threshold. Then, the high-frequency block is reconstructed using effective singular values. The final super-resolution image is restored by the detail synthesis with the initial super-resolution image. The experimental results show that the proposed method can significantly remove the artificial effect of facet or line phenomenon caused by pseudo high-frequency. Moreover, the method is also applicable to other super-resolution algorithm in detail enhancement.