面向真实场景的单帧红外图像超分辨率重建

Single-frame Infrared Image Super-Resolution Reconstruction for Real Scenes

现有的红外图像超分辨率重建方法主要依赖实验数据进行设计,但在面对真实环境中的复杂退化情况时,它们往往无法稳定地表现。针对这一挑战,本文提出了一种基于深度学习的新颖方法,专门针对真实场景下的红外图像超分辨率重建,构建了一个模拟真实场景下红外图像退化的模型,并提出了一个融合通道注意力与密集连接的网络结构。该结构旨在增强特征提取和图像重建能力,从而有效地提升真实场景下低分辨率红外图像的空间分辨率。通过一系列消融实验和与现有超分辨率方法的对比实验,本文方法展现了其在真实场景下红外图像处理中的有效性和优越性。实验结果显示,本文方法能够生成更锐利的边缘,并有效地消除噪声和模糊,从而显著提高图像的视觉质量。

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.