傅里叶变换下的粗细双路径图像修复算法OACSTPCD

In view of the weak ability of extracting global features and the frequency domain difference between the inpainted image and the original image in the traditional coarse and fine dual path image inpainting algorithm,causing the global structure of the repaired image to be poor,the coarse and fine dual path image inpainting algorithm based on Fourier transform is proposed.Firstly,in order to improve the ability of the encoder to extract features,an encoder based on squeeze and excitation model is designed to improve the ability of the network to collect global information.Secondly,the focal frequency loss is introduced for the first time to supervise image inpainting,and the frequency domain difference between the inpainted image and the original image is reduced,it improves the ability of the algorithm to inpaint high-frequency components,and artifacts and blurs are improved.Finally,the algorithm is applied to CelebA dataset.Compared with the images inpainted by the baseline algorithms,the peak signal-to-noise ratio(PNSR)and structural simi-larity(SSIM)of the images inpainted by the proposed algorithm are improved by 1.18%～6.14%and 0.11%～2.24%respec-tively.However,the distance score(FID)is decreased by 34.58%～38.78%.Experimental results show that the proposed algorithm achieves better performance improvement at a small time cost and enhances the global structure and clarity of the inpainted image.