基于深度学习的彩色全息图重建

Color Hologram Reconstruction Based on Deep Learning

针对较大尺寸物体彩色全息图重建操作复杂、色彩融合不准确、重建时受零级影响等问题,提出一种基于深度学习的彩色全息图重建方法。采用改进的U-Net模型作为网络结构,使用混合实际拍摄和模拟生成的彩色离轴菲涅耳全息图频谱作为训练样本,实现对彩色全息图的准确重建。对模拟全息图和实际拍摄的数字全息图进行重建实验,结果表明,所提方法相较于传统方法,能够在保持重建图像高分辨率和颜色准确性的同时,具有更好的重建效果。研究结果可应用于大尺寸检测场彩色全息图的重建,为彩色全息检测及深度学习在光学成像领域中的应用提供有益的参考。

This study proposes a deep learningbased color hologram reconstruction method to address the issues of complex reconstruction operations,inaccurate color fusion,and zeroorder influence during the reconstruction of large objects.The improved UNet model is used as the network structure,and the spectrum of color offaxis Fresnel holograms generated by mixing actual photography and simulation is used as training samples to achieve the accurate reconstruction of color holograms.Reconstruction experiments are conducted on simulated holograms and actual digital holograms.Moreover,the results have shown that compared to traditional methods,the proposed method can maintain high resolution and color accuracy of the reconstructed image while achieving improved reconstruction results.The outcomes of the study have potential applications in the reconstruction of color holograms in largescale inspection fields,and are useful for the application of color holographic detection and deep learning in the field of optical imaging.