¹ Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu 610068, China； Digital media department, Sichuan Normal University, Chengdu 610068, China
² School of Information Engineering, Hubei University for Nationalities, Enshi 445000, China
³ College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China

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Abstract—Increasing image resolution is a challenging and fundamental image-fusion operation, super-resolution a technique to increase image resolution. However, most of method can not reconstruct image from un-regular sampled data. In this paper, we propose a novel super-resolution reconstruction algorithm based on polynomial bases. This algorithm can be combined with intensity information and structural details in the image. The density of sampled data and local structure decide the certainty function and structural-adaptive applicability function of neighbourhood. Experimental results show that the proposed algorithm can improve denoising effect in the super-resolution reconstruction image, and achieve a state-of-the-art high-resolution visual effect in the edges and detailed features of image.

Index Terms—Super-resolution; Subspace projection; Structural-adaptive Applicability Function1

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Cite: Wei-long Chen, Li Guo, and Wen-long Xia, " A Novel Super-resolution Reconstruction Algorithm based on Subspace Projection," Journal of Computers vol. 8, no. 8, pp. 1893-1897, 2013.