基于局部最大梯度与最小强度先验的光场图像去模糊

Deblurring Light Field Images Based on Local Maximum Gradient and Minimum Intensity Priors

空间三维重建在遥感、军事、航空航天等多种领域均有着重要应用。其中,光场成像技术被广泛应用,为了使三维重建更精确,需要对光场成像的每个平面清晰成像。因此,提高光场图像的像质是解决空间重建的关键。首先,将光场成像应用于空间成像系统中,建立基于波动光学的光场成像模型,降低了成像模型复杂性,仿真出光场原始图像。其次,根据光场数字重聚焦算法,进行数字调焦,得到不同对准平面处的光场图像。最后,由于相对运动造成的误差、数字重聚焦算法存在的一定误差和微透镜阵列在光路中成像造成中高频信号损失等因素,重聚焦图像模糊。现有的图像去模糊算法并不能满足光场成像技术所需的像质要求,因此,本文提出一种去除遥感光场重聚焦图像模糊的算法。该算法利用图像模糊会导致局部最小强度值变大和局部最大梯度值变小的先验知识,通过构建能量函数,使用改进的半二次分裂方法估计潜在图像和模糊核,以达到去模糊的目的。结果表明,所提算法在处理光场重聚焦图像上优于其他图像去模糊算法。

Space three-dimensional(3D)reconstruction is important across various domains,including remote sensing,military,and aerospace.Among these,light field imaging technology stands out as widely utilized.Enhancing the image quality of light field images is paramount for achieving more accurate 3D reconstructions.First,integrating light field imaging into space imaging systems and designing a model based on wave optics streamline the imaging process,thereby simulating the original light field image.Subsequently,employing digital refocusing algorithms enables the acquisition of light field images at different focal planes.However,challenges such as errors induced by relative motion,inaccuracies in digital refocusing algorithms,and signal loss due to microlens arrays in the optical path lead to image blurring.Current image deblurring techniques could not fulfil the stringent quality standards of light field imaging.Hence,this study introduces an algorithm to alleviate blurring in remote sensing light field-refocused images.An energy function is constructed by leveraging the insight that image blur correlates with increased local minimum intensity values and decreased local maximum gradient values.An enhanced semi-quadratic splitting method facilitates the estimation of potential images and blur kernels,thus achieving deblurring.Experimental results demonstrate the superiority of the proposed algorithm over existing image deblurring techniques for processing light field-refocused images.