Color image super-resolution algorithm based on SVM classified learning

Jianfei Li; Xiaoping Yang; Zhihong Chen; Haifeng Yang; Jun Liu

doi:10.1117/12.2285456

24 October 2017 Color image super-resolution algorithm based on SVM classified learning

Jianfei Li, Xiaoping Yang, Zhihong Chen, Haifeng Yang, Jun Liu

Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 1046245 (2017) https://doi.org/10.1117/12.2285456
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

Due to the limitations of image capture device and imaging environments in traditional imaging process, high-resolution (HR) images are difficult to be obtained. The method of digital image processing can be used in image super-resolution with one or an image sequence in original conditions to reconstruct HR images which over the range of imaging system. Traditional learning-based super-resolution algorithm need to run through the sample library with a high computing complexity, and a high recognition rate in the scene with small shifts. This dissertation is mainly about color image SR and parallel implementation of the SR algorithm. An algorithm based on SVM classified learning is proposed in this paper.

Citation Download Citation

Jianfei Li, Xiaoping Yang, Zhihong Chen, Haifeng Yang, and Jun Liu "Color image super-resolution algorithm based on SVM classified learning", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046245 (24 October 2017); https://doi.org/10.1117/12.2285456

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