论文标题
图像中空间变化的校正空间变化的盲框反卷积
Blind multi-frame deconvolution for the correction of space-variant blur in images
论文作者
论文摘要
本文展示了一种实用方法，可以从同一对象的一组图像中纠正空间变化的模糊。该算法共同估计对象和局部点扩散函数〜（PSF）。该方法优先考虑PSF中空间变化的部分进行反卷积。这种新颖的方法可以处理本地PSF中的大型翻译，因此该算法能够校正图像中的变形。在数值模拟中证明了对噪声的鲁棒性。在将算法的性能与文献中发现的最新方法进行比较的情况下，进行了数值实验。该算法可以在PSF的空间周期变化的情况下使用，可以在信噪比较低的情况下应用。
This paper demonstrates a practical method that can correct spatial varying blur from a set of images of the same object. The algorithm jointly estimates the object and local point spread functions~(PSF). The method prioritizes sections with small spatial variation in the PSF for deconvolution. This novel approach can handle large translations in the local PSFs, hence the algorithm is able to correct for morph in the images. Robustness to noise is demonstrated in numerical simulations. Numerical experiments are conducted where the performance of the algorithm is compared to a state-of-the-art method found in literature. The algorithm can be used in situation with space-temporal variation of the PSF and can be applied in situations where the signal-to-noise ratio is low.
