An　end-to-end　quadruple　Super-Resolution　Inpainting　Generative　Adversarial　Network　(SRIGAN)　is　proposed　in　this　paper,　for　low-resolution　random　occlusion　face　images.　The　generative　network　consists　of　an　encoder,　a　feature　compensation　subnetwork,　and　a　decoder　constructed　with　a　pyramid　attention　module.　The　discriminant　network　is　an　improved　Patch　discriminant　network.　The　network　can　effectively　learn　the　absent　features　of　the　occluded　region　through　a　feature　compensation　subnetwork　and　a　two-stage　training　strategy.　Then,　the　information　is　constructed　with　the　decoder　with　a　pyramid　attention　module　and　multi-scale　reconstruction　loss.　Hence,　the　generative　network　can　transform　a　low-resolution　occlusion　image　into　a　quadruple　high-resolution　complete　image.　Furthermore,　the　improvements　of　the　loss　function　and　Patch　discriminant　network　are　employed　to　ensure　the　stability　of　network　training　and　enhance　the　performance　of　the　generated　network.　The　effectiveness　of　the　proposed　algorithm　is　verified　by　comparison　and　module　verification　experiments.　©　2024　Science　Press.　All　rights　reserved.