Liu Yuan, Qiu Bingwen, Tian Yayuan, Cai Juan, Sui Xiubao, Chen Qian
Opt Express. 2024 May 6;32(10):16591-16610. doi: 10.1364/OE.515380.
Non-uniformity is a long-standing problem that significantly degrades infrared images through fixed pattern noise (FPN). Existing scene-based algorithms for non-uniformity correction (NUC) effectively eliminate stripe FPN assuming consistent inter-frame non-uniformity. However, they are ineffective in handling spatially continuous optical FPN. In this paper, a scene-based dual domain correction approach is proposed to address the non-uniformity problem by simultaneously removing stripe and optics-caused FPN. We achieve this through gain correction in the frequency domain and offset correction in the spatial domain. To remove stripes, we approximate the desired image using a guided filter and iteratively update the bias correction parameters frame by frame. For optics-caused noise removal, we separate low frequency noise from the scene using Fourier transform and update the gain correction parameters accordingly. To mitigate ghost artifacts, a combined strategy is introduced to adaptively adjusts learning rates and weights during the updating stage. Comprehensive evaluations demonstrate that our proposed approach outperforms compared methods in both real and simulated non-uniformity infrared videos.
非均匀性是一个长期存在的问题，它通过固定模式噪声（FPN）严重降低红外图像质量。现有的基于场景的非均匀性校正（NUC）算法在假设帧间非均匀性一致的情况下，能有效消除条纹状FPN。然而，它们在处理空间连续的光学FPN时效果不佳。本文提出了一种基于场景的双域校正方法，通过同时去除条纹和光学引起的FPN来解决非均匀性问题。我们通过频域增益校正和空域偏移校正来实现这一点。为了去除条纹，我们使用引导滤波器近似期望图像，并逐帧迭代更新偏差校正参数。对于光学噪声去除，我们使用傅里叶变换从场景中分离低频噪声，并相应地更新增益校正参数。为了减轻重影伪像，引入了一种组合策略，在更新阶段自适应调整学习率和权重。综合评估表明，我们提出的方法在真实和模拟的非均匀性红外视频中均优于比较方法。
