In computer vision tasks, dust and fog environments have a severe impact on the visibility and detailed features of images, which restricts the performance of downstream vision tasks. To restore and enhance the details of images degraded by adverse weather conditions, a spatio-temporal frequency domain image restoration and enhancement method is proposed. This method studies the mathematical model of light diffusion under dust and fog atmospheric conditions, uses Gaussian filtering to simulate the diffusion and attenuation effect of the atmosphere on light propagation, constructs a pseudo-time image sequence from the degraded input, and obtains the spatio-temporal frequency domain features of the sequence through Fourier transform in the spatio-temporal dimension. Inspired by the restored pseudo heat flux (RPHF) theory, a frequency domain deconvolution kernel is designed to weight the high-frequency information of the sequence to counteract the degradation effect of atmospheric diffusion on the image detail information. The inverse Fourier transform is performed on the weighted frequency features to reconstruct and enhance the image. To verify this method, a weather dataset containing dust and fog scenes with different degradation intensities is established for experiments. The experimental results show that compared with traditional algorithms, this method performs excellently in medium and severe degradation scenarios (such as the visible edge ratio eof severely degraded fog: 78.990) and can effectively restore images. However, in mild degradation scenarios, due to the large amount of high-frequency information in the images, the indiscriminate amplification of high-frequency information by the method has a negative effect on image quality restoration. Overall, this method is more suitable for the restoration and enhancement of moderately to severely degraded images.