Holographic imaging poses significant challenges when facing real-time disturbances introduced by dynamic environments. The existing deep-learning methods for holographic imaging often depend solely on the specific condition based on the given data distributions, thus hindering their generalization across multiple scenes. One critical problem is how to guarantee the alignment between any given downstream tasks and pretrained models. We analyze the physical mechanism of image degradation caused by turbulence and innovatively propose a swin transformer-based method, termed train-with-coherence-swin (TWC-Swin) transformer, which uses spatial coherence (SC) as an adaptable physical prior information to precisely align image restoration tasks in the arbitrary turbulent scene. The light-processing system (LPR) we designed enables manipulation of SC and simulation of any turbulence. Qualitative and quantitative evaluations demonstrate that the TWC-Swin method presents superiority over traditional convolution frameworks and realizes image restoration under various turbulences, which suggests its robustness, powerful generalization capabilities, and adaptability to unknown environments. Our research reveals the significance of physical prior information in the optical intersection and provides an effective solution for model-to-tasks alignment schemes, which will help to unlock the full potential of deep learning for all-weather optical imaging across terrestrial, marine, and aerial domains.

针对垂直腔面发射激光器阵列光束空间相干特性及其光场分布开展理论分析和实验研究。垂直腔面发射激光器阵列光束为部分相干光，在不同激励电流下，远场光斑分别表现出类高斯和类拉盖尔-高斯两种光场分布，光束空间相干度随注入电流的增大而减小。进一步实验表明，与标准相干光源相比，在类湍流大气散射媒介中传输时，垂直腔面发射激光器阵列光束具有更小光斑扩散和更低光强衰减。随着垂直腔面发射激光器阵列光束空间相干度由0.695降低为0.608，其传输时的光斑扩展率由8.6%减小为3.4%，光强衰减率由24.9%降低为15%，空间相干度较低的垂直腔面发射激光器阵列光束表现出更好的传输特性，这对垂直腔面发射激光器阵列光源在自由空间雷达探测和光通信等领域的应用有重要指导意义。