Anchor neighborhood embedding based single-image super-resolution reconstruction with similarity threshold adjustment
Min Xiong, Yun Song, Yongshen Xiang, Bin Xie, Zhelin Deng · 2021
Anchored neighborhood super-resolution (SR) reconstruction algorithms reconstruct a high-resolution (HR) image from a single low resolution (LR) image effectively by exploiting the non-local similarity in images. In this paper, we propose an anchored neighborhood reconstruction algorithm with a similarity threshold adaptive scheme to improve the reconstruction for the mapping matrix. In the proposed method, a similarity adjustment matrix is introduced to improve the similarity of the image blocks with high deviation in the neighborhood. Besides, a threshold function is applied to determine the weights for similar blocks. Following this function, larger weights are assigned to samples with low deviations and low coefficients are assigned to blocks with low similarity. This scheme is employed to prevent the blocks from being assigned inappropriate weights and benefit the reconstruction. Experimental results show that the proposed algorithm improves the image reconstruction quality with a low computational cost.