Single-frame Infrared Image Super-Resolution Reconstruction for Real Scenes
Current infrared image super-resolution reconstruction methods,which are primarily designed based on experimental data,often fail in complex degradation scenarios encountered in real-world environments.To address this challenge,this paper presents a novel deep learning-based approach tailored for the super-resolution reconstruction of infrared images in real scenarios.The significant contributions of this research include the development of a model that simulates infrared image degradation in real-life settings and a network structure that integrates channel attention with dense connections.This structure enhances feature extraction and image reconstruction capabilities,effectively increasing the spatial resolution of low-resolution infrared images in realistic scenarios.The effectiveness and superiority of the proposed approach for processing infrared images in real-world contexts are demonstrated through a series of ablation studies and comparative experiments with existing super-resolution methods.The experimental results indicate that this method produces sharper edges and effectively eliminates noise and blur,thereby significantly improving the visual quality of the images.
infrared imagedeep learningsuper-resolutionreal scenedegradation model
万方数据
维普
