IEEE Trans Image Process. 2015 Nov;24(11):4637-50. doi: 10.1109/TIP.2015.2461445. Epub 2015 Jul 28.
Deblurring saturated night images are a challenging problem because such images have low contrast combined with heavy noise and saturated regions. Unlike the deblurring schemes that discard saturated regions when estimating blur kernels, this paper proposes a novel scheme to deduce blur kernels from saturated regions via a novel kernel representation and advanced algorithms. Our key technical contribution is the proposed function-form representation of blur kernels, which regularizes existing matrix-form kernels using three functional components: 1) trajectory; 2) intensity; and 3) expansion. From automatically detected saturated regions, their skeleton, brightness, and width are fitted into the corresponding three functional components of blur kernels. Such regularization significantly improves the quality of kernels deduced from saturated regions. Second, we propose an energy minimizing algorithm to select and assign the deduced function-form kernels to partitioned image regions as the initialization for non-uniform deblurring. Finally, we convert the assigned function-form kernels into matrix form for more detailed estimation in a multi-scale deconvolution. Experimental results show that our scheme outperforms existing schemes on challenging real examples.
去饱和夜间图像模糊是一个具有挑战性的问题，因为这些图像对比度低，同时伴有严重的噪声和过饱和区域。与在估计模糊核时丢弃过饱和区域的去模糊方案不同，本文提出了一种新颖的方案，通过新的核表示和先进的算法，从过饱和区域推导出模糊核。我们的关键技术贡献是提出的模糊核函数形式表示，它使用三个功能组件对现有矩阵形式的核进行正则化：1）轨迹；2）强度；3）扩展。从自动检测到的过饱和区域中，提取出它们的骨架、亮度和宽度，拟合到模糊核的相应三个功能组件中。这种正则化显著提高了从过饱和区域推导出的核的质量。其次，我们提出了一种能量最小化算法，将推导出的函数形式核选择并分配给分区图像区域，作为非均匀去模糊的初始值。最后，我们将分配的函数形式核转换为矩阵形式，以便在多尺度反卷积中进行更详细的估计。实验结果表明，我们的方案在具有挑战性的真实示例上优于现有方案。
