Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2022 Apr 13;22(8):2971. doi: 10.3390/s22082971.
Infrared images often carry obvious streak noises due to the non-uniformity of the infrared detector and the readout circuit. These streak noises greatly affect the image quality, adding difficulty to subsequent image processing. Compared with current elimination algorithms for infrared stripe noises, our approach fully utilizes the difference between the stripe noise components and the actual information components, takes the gradient sparsity along the stripe direction and the global sparsity of the stripe noises as regular terms, and treats the sparsity of the components across the stripe direction as a fidelity term. On this basis, an adaptive edge-preserving operator (AEPO) based on edge contrast was proposed to protect the image edge and, thus, prevent the loss of edge details. The final solution was obtained by the alternating direction method of multipliers (ADMM). To verify the effectiveness of our approach, many real experiments were carried out to compare it with state-of-the-art methods in two aspects: subjective judgment and objective indices. Experimental results demonstrate the superiority of our approach.
红外图像由于红外探测器和读出电路的不均匀性，常常带有明显的条纹噪声。这些条纹噪声极大地影响了图像质量，给后续的图像处理带来了困难。与现有的红外条纹噪声消除算法相比，我们的方法充分利用了条纹噪声分量和实际信息分量之间的差异，将条纹方向上的梯度稀疏性和条纹噪声的全局稀疏性作为正则项，并将跨条纹方向的分量稀疏性作为保真度项。在此基础上，提出了一种基于边缘对比度的自适应边缘保持算子（AEPO），以保护图像边缘，从而防止边缘细节的丢失。最终的解通过交替方向乘子法（ADMM）得到。为了验证我们方法的有效性，进行了许多真实实验，并从主观判断和客观指标两个方面与最先进的方法进行了比较。实验结果表明了我们方法的优越性。
