Abstract
High-frequency information such as image edges and textures have an important influence on the visual effect of the super-resolution images. Therefore, it is vital to maintain the edge and texture features of the super-resolution image. A surface fitting image super-resolution algorithm based on triangle mesh partitions is proposed in this study. Different from the traditional image interpolation algorithm using quadrilateral mesh, this method reconstructs the fitting surface on the triangle mesh to approximate the original scene surface. LBP algorithm and second-order difference quotient are combined to divide the triangular mesh accurately, and the edge angle is utilized as a constraint to makes the edge of the constructed surface patch more informative. By the area coordinates as weighting coefficients to perform weighted averaging on the surface patches at the vertices of the triangle mesh, the cubic polynomial surface patches are obtained on the triangle mesh. Finally, a global structure sparse regularization strategy is adopted to optimize the initially super-resolution image and further eliminate the artifacts at the image edges and textures. Since the new method proposed in this study utilizes numerous information about local feature (e.g. edges), compared to other state-of-the-art methods, it provides clear edges and textures, and improves the image quality greatly.
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Xu, H., Ye, C., Feng, N., Zhang, C. (2021). A Surface Fitting Image Super-Resolution Algorithm Based on Triangle Mesh Partition. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_8
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