IEEE Trans Image Process. 2015 Nov;24(11):4433-45. doi: 10.1109/TIP.2015.2465162. Epub 2015 Aug 5.
Out-of-focus blur occurs frequently in multispectral imaging systems when the camera is well focused at a specific (reference) imaging channel. As the effective focal lengths of the lens are wavelength dependent, the blurriness levels of the images at individual channels are different. This paper proposes a multispectral image deblurring framework to restore out-of-focus spectral images based on the characteristic of interchannel correlation (ICC). The ICC is investigated based on the fact that a high-dimensional color spectrum can be linearly approximated using rather a few number of intrinsic spectra. In the method, the spectral images are classified into an out-of-focus set and a well-focused set via blurriness computation. For each out-of-focus image, a guiding image is derived from the well-focused spectral images and is used as the image prior in the deblurring framework. The out-of-focus blur is modeled as a Gaussian point spread function, which is further employed as the blur kernel prior. The regularization parameters in the image deblurring framework are determined using generalized cross validation, and thus the proposed method does not need any parameter tuning. The experimental results validate that the method performs well on multispectral image deblurring and outperforms the state of the arts.
当相机在特定（参考）成像通道上对焦良好时，多光谱成像系统经常会出现离焦模糊。由于透镜的有效焦距随波长而变化，因此各个通道的图像模糊程度不同。本文提出了一种基于通道间相关性（ICC）特征的多光谱图像去模糊框架，用于恢复离焦光谱图像。ICC 是基于这样一个事实来研究的，即高维彩色光谱可以使用相当少的固有光谱进行线性近似。在该方法中，通过模糊度计算将光谱图像分类为离焦集和聚焦良好集。对于每个离焦图像，从聚焦良好的光谱图像中导出一个导向图像，并将其用作去模糊框架中的图像先验。离焦模糊被建模为高斯点扩散函数，进一步用作模糊核先验。图像去模糊框架中的正则化参数使用广义交叉验证确定，因此该方法不需要任何参数调整。实验结果验证了该方法在多光谱图像去模糊方面的性能良好，优于现有技术。
