Yu Yang, Jiang Lin, Hu Qijun, Cai Qijie, Zeng Qiang, Sun Xin
School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, 610500, Sichuan, China.
School of Civil Engineering, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China.
Sci Rep. 2025 Jul 1;15(1):21423. doi: 10.1038/s41598-025-06538-0.
Infrared imaging technology is vital for security monitoring, industrial detection, and medical diagnosis. However, atmospheric thermal radiation degrades its quality, causing contrast reduction, texture blurring, and non-uniform noise. To address these challenges, this paper introduces a novel infrared image enhancement method using a dual decoding generative adversarial network (2D-GAN). First, internal and external skip connections are designed to enhance high-frequency detail transmission and mitigate gradient vanishing in deep networks, with local details being preserved as a result. Second, a cross-layer attention mechanism is proposed to adaptively adjust feature map weights spatially and across channels, with information loss during encoding-decoding being minimized and texture clarity and structural coherence being improved. Finally, a joint loss function is designed to integrate pixel-level accuracy, semantic consistency, and global structural coherence, with image realism and perceptual quality being enhanced consequently. Experiments demonstrate superior performance over existing methods in comparative and ablation studies on public datasets, confirming excellent enhancement capabilities and generalization.
红外成像技术在安全监控、工业检测和医学诊断中至关重要。然而，大气热辐射会降低其质量，导致对比度降低、纹理模糊和非均匀噪声。为应对这些挑战，本文介绍了一种使用双解码生成对抗网络（2D-GAN）的新型红外图像增强方法。首先，设计了内部和外部跳跃连接，以增强高频细节传输并减轻深度网络中的梯度消失，从而保留局部细节。其次，提出了一种跨层注意力机制，以在空间和跨通道上自适应地调整特征图权重，将编码-解码过程中的信息损失降至最低，并提高纹理清晰度和结构连贯性。最后，设计了一个联合损失函数，以整合像素级精度、语义一致性和全局结构连贯性，从而提高图像的真实感和感知质量。实验表明，在公共数据集的比较和消融研究中，该方法优于现有方法，证实了其卓越的增强能力和泛化能力。
