Image super-resolution based on compressive sensing

Ying Gu; Xiuchang Zhu

doi:10.1117/12.913513

1 October 2011 Image super-resolution based on compressive sensing

Ying Gu, Xiuchang Zhu

Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 828575 (2011) https://doi.org/10.1117/12.913513
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt

Abstract

Based on Compressive Sensing, we introduce sparse signal representation theory to modify the local geometric similarity model and construct sparse geometric similarity representation. Based on the modified model we can estimate the optimized reconstruct coefficients by jointing the original global and local image structure themselves, without the support of other training image database. The experimental results show that the algorithm can greatly improve the reconstruction of the edge and texture details in the high-resolution image.

Citation Download Citation

Ying Gu and Xiuchang Zhu "Image super-resolution based on compressive sensing", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 828575 (1 October 2011); https://doi.org/10.1117/12.913513

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