Image inpainting algorithm of generative adversarial network based on multi feature fusion

Aiming at problems in existing image inpainting algorithms, such as inconsistent structure and blurred texture, an image inpainting algorithm of generative adversarial network based on multi feature fusion was proposed. This algorithm introduces a multi feature fusion module (CAMFM) that combines coordinate attention mechanism in traditional generators to obtain larger receptive fields and multi-scale features. In addition, the generator is designed with a dual encoding structure and introduces attention for image feature extraction. The VGG19 network is introduced in the generator to extract features for calculating perceptual loss and style loss. Verified on the CelebA dataset, the peak signal-to-noise ratio (PSNR) of the repair results is 28.75dB, the structural similarity (SSIM) is 0.938, and the Fréchet Inception distance (FID) is 5.99. Compared with the four benchmark algorithms, the algorithm proposed in the article showed the best performance in all three indicators, proving that the algorithm proposed in the article has good repair performance.