Abstract
Camera lens distortions are widely observed in different applications for achieving specific optical effects, such as wide angle captures. Moreover, the image with lens distortion is often limited in resolution due to the cost of camera, limited bandwidth, etc. In this paper, we present a learning-based image super-resolution method for improving the resolution of images captured by cameras with barrel lens distortions. The key to the significant improvement of the resolution loss due to lens distortions is to learn a sparse dictionary with a post-processing step. During the training stage, the training images are used to learn the sparse dictionary and projection matrixes. During the testing stage, the observed low-resolution image uses the projection matrixes for two step super-resolution reconstructions of the final high-resolution image. Experimental results show that the proposed method outperforms the conventional learning-based super-resolution methods in terms of PSNR and SSIM values using the same set of training images for algorithm trainings.
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Acknowledgments
This work was supported in part by the Shenzhen Emerging Industries of the Strategic Basic Research Project (No. JCYJ20160226191842793), and the National Natural Science Foundation of China (Nos. 61602312, 61602314, 61620106008).
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Su, M., Hung, KW., Jiang, J. (2017). Super-Resolution for Images with Barrel Lens Distortions. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_21
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DOI: https://doi.org/10.1007/978-3-319-63558-3_21
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