Light field global optimization with pixel-voxel distribution learning: a strategy for diffusion suppression in integral imaging

To address voxel diffusion in integral imaging caused by multiple rotation errors in the lens array, a global optimization method based on homologous pixel-voxel distribution learning of a generative adversarial network (PVGAN) is proposed. By establishing a 3D resolution degradation model under multiple rotation errors, this paper breaks the bottlenecks by proposing the quantitative characterization of multi-error coupling mechanisms for what we believe to be the first time, overcoming the inherent limitations of conventional methods in holistic error-system modeling. The PVGAN framework consists of pixel rearrangement and pixel-to-pixel conditional generative adversarial network modules. The former module establishes the geometric correspondence between preprocessed voxels and the homologous pixels on the element image arrays, while the latter enables end-to-end voxel diffusion correction. Notably, the trained model is capable of batch-processing the voxel diffusion without requiring iterative preprocessing stages. Optical experiments reveal that the method achieves high-quality displays with a horizontal and vertical resolution of 16 lp/mm over a 40° field of view, which is 3.174 and 4 times better than the uncorrected system, respectively. Compared with the existing single-error correction methods, the proposed method significantly suppresses voxel diffusion while improving edge texture feature clarity, which provides stronger support for accurate battlefield simulation and instructional demonstration.