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摘要
Proposed in this paper is a constrained stepwise magnification strategy for the super-resolution image reconstruction based on the neighbor embedding, which is used to increase the neighborhood-preserving rate and improve the reconstruction effect. Then, the iterative back-projection constraint is used to modify the magnified image in each step, which decreases the errors that may occur during the learning procedure and makes the solution of each step evolve in a correct direction. Moreover, in order to take full advantage of the information of the test image, a joint training set is constructed by concatenating the on-line training set learned from the test image and the offline training set learned from the training image database, and is used to further improve the algorithm performance. Experimental results show that, as compared with some existing algorithms, the proposed algorithm helps to obtain better visual effect and more objective evaluation results.
