Liang Jian-An, Guo Ya-Fei, Liu Bin
Opt Express. 2022 Jun 6;30(12):22107-22122. doi: 10.1364/OE.457993.
Color split-focal plane polarization imaging systems are composed of image sensors with a color polarization filter array (CPFA). The noise generated during image acquisition leads to incorrect estimation of the color polarization information. Therefore, it is necessary to denoise CPFA image data. In this study, we propose a CPFA block-matching and 3D filtering (CPFA-BM3D) algorithm for CPFA image data. The algorithm makes full use of the correlation between different polarization channels and different color channels, restricts the grouping of similar 2D image blocks to form 3D blocks, and attenuates Gaussian noise in the transform domain. We evaluate the denoising performance of the proposed algorithm using simulated and real CPFA images. Experimental results show that the proposed method significantly suppresses noise while preserving the image details and polarization information. Its peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indicators are superior to those of the other existing methods. The mean values of the PSNR and SSIM of the degree of linear polarization (DoLP) color images calculated through CPFA image interpolation can be increased to 200% and 400%, respectively, by denoising with the proposed method.
彩色分焦平面偏振成像系统由带有彩色偏振滤波器阵列（CPFA）的图像传感器组成。图像采集过程中产生的噪声会导致对彩色偏振信息的错误估计。因此，有必要对CPFA图像数据进行去噪。在本研究中，我们针对CPFA图像数据提出了一种CPFA块匹配与三维滤波（CPFA-BM3D）算法。该算法充分利用不同偏振通道和不同颜色通道之间的相关性，限制相似二维图像块的分组以形成三维块，并在变换域中衰减高斯噪声。我们使用模拟的和真实的CPFA图像评估了所提算法的去噪性能。实验结果表明，所提方法在保留图像细节和偏振信息的同时显著抑制了噪声。其峰值信噪比（PSNR）和结构相似性（SSIM）指标优于其他现有方法。通过使用所提方法进行去噪，通过CPFA图像插值计算得到的线性偏振度（DoLP）彩色图像的PSNR和SSIM的平均值分别可以提高到200%和400%。
