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.