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A local structure adaptive super-resolution reconstruction method based on BTV regularization

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Abstract

Super-resolution (SR) image reconstruction has been one of the hottest research fields in recent years. The main idea of SR is to utilize complementary information from a set of low resolution (LR) images of the same scene to reconstruct a high-resolution image with more details. Under the framework of the regularization based SR, this paper presents a local structure adaptive BTV regularization based super-resolution reconstruction method to overcome the shortcoming of the Bilateral Total Variation (BTV) super resolution reconstruction model. The proposed method adaptively chooses prior model and regularization parameter according to the local structures. Experimental results show that the proposed method can get better reconstruction results and significantly reduces the manual workload of the regularization parameter selection.

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Lu Zhou, Xiaobo Lu & Li Yang

Authors
  1. Lu Zhou
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  2. Xiaobo Lu
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  3. Li Yang
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Corresponding author

Correspondence to Xiaobo Lu.

Additional information

This work was supported by National Natural Science Foundation of China under grant 60972001 and National Key Technologies R & D Program of China under grant 2009BAG13A06.

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Zhou, L., Lu, X. & Yang, L. A local structure adaptive super-resolution reconstruction method based on BTV regularization. Multimed Tools Appl 71, 1879–1892 (2014). https://doi.org/10.1007/s11042-012-1311-x

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