Image dehazing method combining diffusion model and Fourier filter
The current research on image dehazing algorithms mainly focuses on building intricately structured neural networks.There is a lack of research on using diffusion models and generative methods for image dehazing,and there is also relatively little research on combining diffusion models with frequency domain analysis.This results in many algorithms having poor dehazing effects when processing real hazy images.In response to the above issues,a Firs-Net image dehazing method is proposed based on the score matching diffusion model IR-SDE.This method utilizes the powerful image generation ability of diffusion models to gradually restore clear and hazy-free images from hazy images in an iterative manner.Firs-Net introduces a Fourier feature fusion module,which can help the diffusion models better analyze and fuse features from a frequency domain perspective without increasing the number of parameters and fine-tuning the neural network.The experimental results show that Firs-Net performs excellently in both subjective visual and objective indicators on real hazy datasets,with a PSNR of 21.91 in the NH-HAZE dataset of real non-uniform haze,and a PSNR of 17.40 in the real dense haze dataset Dense-HAZE,which is 13.94%and 5.52%ahead of the suboptimal method,respectively.
万方数据
维普
