Laser spot images are easily limited by imaging conditions and imaging methods during the imaging process, resulting in low resolution of laser spot images and difficulty in meeting practical needs. Therefore, a super-resolution reconstruction method for laser spot images based on visual communication technology is proposed. The visual communication technology is used to collect the laser spot image, and the dual tree complex wavelet threshold method is used to denoise the laser spot image. The dense neural network is improved to extract the characteristics of the laser spot image, the number of atoms in the dictionary is reduced based on the singular value decomposition method, and the sparse expression regularization method is improved to achieve the super-resolution reconstruction of the laser spot image. The experimental results show that the low resolution image reconstruction results of the proposed method are closer to the original image, and the structural similarity of the reconstructed images is above 0.9, proving that the reconstruction effect of this method is good and more suitable for practical applications.