Regular Paper

[OPTICAL REVIEW Vol. 21, No. 1 (2014) 35-42]
© 2014 The Japan Society of Applied Physics

Super-resolution Image Reconstruction Based on Tukey Data Fusion and Bilateral-Total-Variation Regularization

Yan CHEN1*, Shuhua WANG1, Weiqi JIN2, Guangping WANG1, Weili CHEN1, and Junwei LI1

1Science and Technology on Optical Radiation Laboratory, Beijing 100854, China
2Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China

(Received August 15, 2013; Accepted November 21, 2013)

Images of high-resolution are desired and often required in most photoelectronic imaging applications, and corresponding image reconstruction algorithm has became the frontier topics. On the basis of stochastic theory, a novel super-resolution image reconstruction algorithm based on Tukey norm data fusion and bilateral total variation regularization is proposed in this paper. The Tukey norm is employed for fusing the data of low-resolution frames and removing outliers in the data, and then aiming at the sickness of super-resolution reconstruction, the bilateral total variation regularization as a priori knowledge about the solution is incorporated to remove the artifacts from the final answer and improve the convergence rate. Simulated and real experiment results show that the proposed algorithm can improve the image resolution greatly and it is immune to noise and errors in motion and blur estimation.

Key words: super-resolution, reconstruction, Tukey, data fusion, regularization

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