Laser active imaging technology plays an important role in many fields. However, due to the interference of various factors during the imaging process, images may generate noise, which affects subsequent information extraction and processing. Therefore, a laser active imaging visual image denoising method based on wavelet neural network is designed. Design a super-resolution reconstruction method based on image registration algorithm, which concentrates multiple frames of spot images in each subregion to correct the differences between images. Given that the color images captured by laser imaging are rich in primary colors, this leads to a huge amount of data and efficiency bottlenecks in processing. In order to optimize the subsequent preprocessing and recognition process, the average method is used to implement grayscale processing of the image. Design a wavelet neural network structure with a single hidden layer structure, with only one node set in the input layer to receive input information, and only one node set in the output layer to output processed results. Determine the number of hidden layer nodes according to the design method, and take the number of samples as the value of the number of image pixels used for learning, to achieve denoising processing of laser active imaging visual images. The experimental test results show that the denoised image of the design method is relatively clear while retaining image details. The difference in range index is small, and the pixel distribution of the denoised image is relatively uniform.