24 October 2017 Single-image super-resolution based on sparse kernel ridge regression
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Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 1046203 (2017) https://doi.org/10.1117/12.2281290
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Because they are affected by imaging conditions, aliasing, noise, etc, imaging systems are unable to obtain all of the information contained in an original scene. Super-resolution (SR) reconstruction is important for the application of image data to increase the resolution of images. In this article, an example-based algorithm is proposed to implement SR reconstruction by single-image. The mapping function between low-resolution (LR) and high-resolution (HR) images is learned by using the method of regularized regression. Then, finding the optimal sparse subset of the training data set by kernel matching pursuit (KMP). The results show that this method can recover detailed information of images, and the computational cost is reduced compared to other example-based SR methods.
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Fanlu Wu, Fanlu Wu, Xiangjun Wang, Xiangjun Wang, } "Single-image super-resolution based on sparse kernel ridge regression", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046203 (24 October 2017); doi: 10.1117/12.2281290; https://doi.org/10.1117/12.2281290

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