23 January 2017 Half-blind remote sensing image restoration with partly unknown degradation
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Proceedings Volume 10322, Seventh International Conference on Electronics and Information Engineering; 1032213 (2017) https://doi.org/10.1117/12.2265349
Event: Seventh International Conference on Electronics and Information Engineering, 2016, Nanjing, China
Abstract
The problem of image restoration has been extensively studied for its practical importance and theoretical interest. This paper mainly discusses the problem of image restoration with partly unknown kernel. In this model, the degraded kernel function is known but its parameters are unknown. With this model, we should estimate the parameters in Gaussian kernel and the real image simultaneity. For this new problem, a total variation restoration model is put out and an intersect direction iteration algorithm is designed. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) are used to measure the performance of the method. Numerical results show that we can estimate the parameters in kernel accurately, and the new method has both much higher PSNR and much higher SSIM than the expectation maximization (EM) method in many cases. In addition, the accuracy of estimation is not sensitive to noise. Furthermore, even though the support of the kernel is unknown, we can also use this method to get accurate estimation.
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Meihua Xie, Meihua Xie, Fengxia Yan, Fengxia Yan, } "Half-blind remote sensing image restoration with partly unknown degradation", Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 1032213 (23 January 2017); doi: 10.1117/12.2265349; https://doi.org/10.1117/12.2265349
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