Abstract
Considering that there is no edge constraint in general regularized algorithms, an improved super-resolution algorithm with additional regularization is presented. The Difference Curvature (DC) regularization which containing the image edge information is joined into the cost function, for further preserving the edge details at image reconstruction procedure. In each iteration, the DC regularization will extract the edge of high-resolution prediction frame and low-resolution observation frame. And the error between them is used to compensate the edge loss which may be smoothed by existing regularization. The reconstructed result is approximated to the original image by constraining the error between them. Then the optimum solution will be worked out by utilizing the steepest descent method. This approach is intended to constrain the edges of the image directly rather than simply avoiding the edge being smoothed. Comparing with other single regularization algorithms, experiment results indicate that the proposed algorithm can restore the edge details of reconstructed image well. And it also shows that various prior knowledge are important to image reconstruction process.
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Luo, Z., Chen, D., Wang, X. (2018). Regularized Super-Resolution Reconstruction Based on Edge Prior. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_44
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DOI: https://doi.org/10.1007/978-3-319-95957-3_44
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