Correlation reconstruction mechanism based on dual wavelength imaging and neural network

Abstract

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.