Global Context-Aware Method for Infrared Image Non-Uniformity Correction

Infrared imaging systems are often affected by non-uniformity, which can significantly reduce the quality of captured images and hinder subsequent image-processing tasks. Existing infrared image non-uniformity correction methods mainly suffer from incomplete detail recovery and easy ghosting. This paper proposes a global context-aware infrared image non-uniformity correction method, which effectively captures long-distance dependencies and complex feature patterns by combining residual networks and self-attention mechanisms. To verify the effectiveness of our method, we conducted extensive experiments on multiple public datasets. Experimental results show that compared with the existing state-of-the-art technology, our method performs better in both qualitative and quantitative evaluations and can effectively remove infrared image stripe noise in complex scenes while retaining key details of the image.