Boutemedjet Ayoub, Deng Chenwei, Zhao Baojun
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2018 Apr 11;18(4):1164. doi: 10.3390/s18041164.
The problem of stripe non-uniformity in array-based infrared imaging systems has been the focus of many research studies. Among the proposed correction techniques, total variation models have been proven to significantly reduce the effect of this type of noise on the captured image. However, they also cause the loss of some image details and textures due to over-smoothing effect. In this paper, a correction scheme is proposed based on unidirectional variation model to exploit the direction characteristic of the stripe noise, in which an edge-aware weighting is incorporated to convey image structure retaining ability to the overall algorithm. Moreover, a statistical-based regularization is also introduced to further enhance correction performance around strong edges. The proposed approach is thoroughly scrutinized and compared to the state-of-the-art de-striping techniques using real stripe non-uniform images. Results demonstrate a significant improvement in edge preservation with better correction performance.
基于阵列的红外成像系统中的条纹不均匀问题一直是众多研究的焦点。在提出的校正技术中，总变差模型已被证明能显著降低此类噪声对捕获图像的影响。然而，由于过度平滑效应，它们也会导致一些图像细节和纹理的丢失。本文提出了一种基于单向变差模型的校正方案，以利用条纹噪声的方向特性，其中引入了边缘感知加权，将图像结构保留能力传递给整个算法。此外，还引入了基于统计的正则化，以进一步增强强边缘周围的校正性能。使用真实的条纹不均匀图像对所提出的方法进行了全面审查，并与当前最先进的去条纹技术进行了比较。结果表明，在边缘保留方面有显著改进，校正性能更好。
