Adaptive Super-Resolution Image Reconstruction Algorithm of Neighborhood Embedding Based on Nonlocal Similarity

Super resolution technology origins from the field of image restoration. The increasing difficulties in the resolution improvement by hardware prompts the super resolution reconstruction that can solve this problem effectively, but the general algorithms of super resolution reconstruction model are unable to quickly complete the image processing. Based on this problem, this paper studies on adaptive super-resolution reconstruction algorithm of neighbor embedding based on nonlocal similarity, in the foundation of traditional neighborhood embedding super resolution reconstruction method, using nonlocal similarity clustering algorithm, classifying the image training sets, which reduces the matching search complexity and speeds up the algorithm; by introducing new characteristic quantity and building a new calculation formula for solving weights, the quality of reconstruction is enhanced. The simulation test shows that the algorithm proposed in this paper is superior to the traditional regularization method and the spline interpolation algorithm no matter on the objective index about statistic and structural features or subjective evaluation.