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Remote Sensing Image Super-Resolution Based on Lorentz Fitting

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Abstract

Due to the effect of the model mismatch error between the point spread function (PSF) and actual blur kernel, the performance of remote sensing image super-resolution (SR) is usually poor. In this paper, we propose a novel remote sensing image super-resolution method based on Lorentz fitting, to improve the reconstruction performance in actual application. Note that the actual blur kernel of remote sensing image is non-stationary and usually has non-smooth phenomenon in kernel edge. It is also asymmetric that the degree of blur is not same in radial and tangential directions, and blur is in a sense that the amount of blur depends on pixel locations in a sensor. This paper presents a flexible parametric blur kernel model based on a linear combination of Lorentz basic two-dimensional (2-D) patterns. The proposed model can provide flexible shapes for blur kernel with a different symmetry and non-smooth edge, which can model complicated blur due to various degradation factors accurately. Combining with the proposed PSF model and the optimized adaptive step size strategy, we proposed a remote sensing image super-resolution method to accelerate the convergence of super-resolution outputs. Experiment results have shown that the proposed method outperforms the other recent developed PSF model based remote sensing image super-resolution methods.

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Funding

This work was supported by the Natural Science Foundation of Zhejiang Province (No. LQ21F010014) and the National Natural Science Foundation of China (NSFC, No. 61871348).

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Authors and Affiliations

  1. Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Guoxing Huang, Yipeng Liu, Weidang Lu, Yu Zhang & Hong Peng

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  1. Guoxing Huang
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  2. Yipeng Liu
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  3. Weidang Lu
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  4. Yu Zhang
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  5. Hong Peng
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Correspondence to Weidang Lu.

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Huang, G., Liu, Y., Lu, W. et al. Remote Sensing Image Super-Resolution Based on Lorentz Fitting. Mobile Netw Appl 27, 1615–1628 (2022). https://doi.org/10.1007/s11036-021-01870-x

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