Ye Hualong, Guo Daidou
School of Electrical and Information Engineering, Changshu Institute of Technology, Changshu, 215500, China.
University of Shanghai for Science and Technology, Shanghai, 200093, China.
Sci Rep. 2024 Dec 2;14(1):18241. doi: 10.1038/s41598-024-68881-y.
In a natural light-field imaging scene, lighting is usually performed using a mixed light field of multiple wavelengths. To better meet the requirements of human vision, this study proposes a correlation reconstruction method based on dual-wavelength imaging and a neural network (compression sensing correlation fusion-reconstruction by dual-wavelength imaging and auto-encoder neural network, CSCFR-DWI-AENN). Two different wavelengths of light are mixed through an optical multiplexer unit (OMU) to form a dual-wavelength illumination light field, and the light intensity value reflected by the object is received by the detector. The object information was reconstructed using the compressed sensing ghost imaging algorithm, and the distribution of the illumination field was taken as prior information. Two single-wavelength illumination information and the detection value are used for compressed sensing reconstruction, and the high-quality reconstructed image of the target to be measured is obtained by combining the noise reduction advantage of the autoencoder neural network. Then the target information is fused by the New Sum of Modified Laplacian (NSML) algorithm. Two single-wavelength reconstruction, dual-wavelength direct reconstruction and the proposed reconstruction were compared respectively, and non-reference image quality evaluation functions such as EN, MI, EAV and SF were used to process and analyze the reconstruction effect. It is proved that this algorithm can reconstruct the object image comprehensively, reproduce the complete detail information, and achieve a more accurate, more comprehensive and more reliable description of the same object. It provides a theoretical basis for multi-wavelength imaging technology and has a good application prospect.
在自然光场成像场景中，通常使用多波长混合光场进行照明。为了更好地满足人类视觉需求，本研究提出了一种基于双波长成像和神经网络的相关重建方法（双波长成像与自动编码器神经网络的压缩感知相关融合重建，CSCFR-DWI-AENN）。两种不同波长的光通过光学复用器单元（OMU）混合形成双波长照明光场，探测器接收物体反射的光强值。利用压缩感知鬼成像算法重建物体信息，并将照明场的分布作为先验信息。利用两个单波长照明信息和检测值进行压缩感知重建，结合自动编码器神经网络的降噪优势，得到待测目标的高质量重建图像。然后通过改进拉普拉斯新和（NSML）算法对目标信息进行融合。分别比较了两种单波长重建、双波长直接重建和所提出的重建方法，并使用EN、MI、EAV和SF等非参考图像质量评价函数对重建效果进行处理和分析。结果表明，该算法能够全面重建物体图像，再现完整的细节信息，实现对同一物体更准确、更全面、更可靠的描述。为多波长成像技术提供了理论依据，具有良好的应用前景。
