Research on Laser Polarization Image Reconstruction Based on Wavelet Transform and Deep Learning

The traditional laser polarization image recoustruction method is affected by environmental noise, resalting in poor image reconstruction effect. For this reason, a wavelet transform and deep learning laser polarization image recoustruction method is designed. The convolutional neural network is ased to denoise the image, the wavelet transform method is ased to ertract the image terture featares, and the overall nested network edge detection method in deep learning is introdaced to detect the edge. In addition, the featare fasion modale in the wavelet transform is ased for processing, adding Multiscale Dilated Dense Block MDDB, Erperimental Laser Polarization Image Reconstruction. The erperimental comparison resalts show that the method proposed in this paper can accurately identify the target in the image, malse foll ase of the activation function in it to learn and identify the image featares, effectively prevent the loss of important information in the image feature learning and identification. This method significantly improves the quality of reconstructed images and achieves better visual effects.