To address the issues inherent in existing image and video restoration and enhancement techniques,this paper proposes a neural network model approach rooted in semantic feature extraction.Firstly,an image restoration and enhancement framework centered on semantic feature is introduced,followed by the joint optimization of degradation and reconstruction models.The proposed model is validated on a publicly accessible dataset and compared with existing algorithms.The results indicate that the proposed approach achieves a 50% improvement in RankIQA (Rank Image Quality Assessment) scores compared to the state-of-the-art super-resolution algorithm PULSE (Photo Upsampling via Latent Space Exploration).Furthermore,the quality scores of the enhanced images and videos are comparable to those of the original HD ones.In terms of user evaluation,81% of the reconstructed results are considered to be superior to those produced by the comparison algorithms,demonstrating that the proposed approach offers higher quality in reconstructed images and videos.