19 December 2013 Polarization image super-resolution with non-local sparse models
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Proceedings Volume 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 90451B (2013) https://doi.org/10.1117/12.2036656
Event: International Conference on Optical Instruments and Technology (OIT2013), 2013, Beijing, China
Abstract
The different polarization phase angle (orientation) low-resolution images of the same scene have much redundant and complementary information which can be used to construct a high-resolution image. In this paper, we propose a super-resolution (SR) algorithm via sparse and redundant representation with considering the non-local self-similarity in different polarization orientation images. As the redundant over-complete dictionary has many irrelevant atoms which not only reduce the computational efficiency in sparse coding but also reduce the representation accuracy, we learn a local dictionary by applying the principal component analysis (PCA) technique. For an image patch to be coded, the best fitted sub-dictionary is adaptively selected by an adaptive sparse domain selection strategy. To improve the stability and accuracy of sparse coding, the centralized sparse coding algorithm is used. The extensive experimental results demonstrated that the proposed method can effectively reconstruct the polarization image with edge structure preserved and detailed information obtained in terms of PSNR, SSIM and visual perception.
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Guo-ming Xu, Guo-ming Xu, Meng-zi Zhang, Meng-zi Zhang, Guo-chun Zhu, Guo-chun Zhu, Lei-ji Lu, Lei-ji Lu, } "Polarization image super-resolution with non-local sparse models", Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 90451B (19 December 2013); doi: 10.1117/12.2036656; https://doi.org/10.1117/12.2036656
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