Zhao Xiaohang, Li Mingxuan, Nie Ting, Han Chengshan, Huang Liang
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). 2023 Jul 29;23(15):6786. doi: 10.3390/s23156786.
The non-uniformity of infrared detectors' readout circuits can lead to stripe noise in infrared images, which affects their effective information and poses challenges for subsequent applications. Traditional denoising algorithms have limited effectiveness in maintaining effective information. This paper proposes a multi-level image decomposition method based on an improved LatLRR (MIDILatLRR). By utilizing the global low-rank structural characteristics of stripe noise, the noise and smooth information are decomposed into low-rank part images, and texture information is adaptively decomposed into several salient part images, thereby better preserving texture edge information in the image. Sparse terms are constructed according to the smoothness of the effective information in the final low-rank part of the image and the sparsity of the stripe noise direction. The modeling of stripe noise is achieved using multi-sparse constraint representation (MSCR), and the Alternating Direction Method of Multipliers (ADMM) is used for calculation. Extensive experiments demonstrated the proposed algorithm's effectiveness and compared it with state-of-the-art algorithms in subjective judgments and objective indicators. The experimental results fully demonstrate the proposed algorithm's superiority and efficacy.
红外探测器读出电路的不均匀性会导致红外图像中出现条纹噪声，这会影响图像的有效信息，并给后续应用带来挑战。传统的去噪算法在保持有效信息方面效果有限。本文提出了一种基于改进的LatLRR的多级图像分解方法（MIDILatLRR）。通过利用条纹噪声的全局低秩结构特征，将噪声和平滑信息分解为低秩部分图像，将纹理信息自适应分解为多个显著部分图像，从而更好地保留图像中的纹理边缘信息。根据图像最终低秩部分中有效信息的平滑性和条纹噪声方向的稀疏性构建稀疏项。使用多稀疏约束表示（MSCR）对条纹噪声进行建模，并使用交替方向乘子法（ADMM）进行计算。大量实验证明了所提算法的有效性，并在主观判断和客观指标方面将其与现有算法进行了比较。实验结果充分证明了所提算法的优越性和有效性。
