Yang Meng, Wang Fei, Wang Yibin, Zheng Nanning
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2017 Nov 30;17(12):2778. doi: 10.3390/s17122778.
Intensified charge-coupled device (ICCD) images are captured by ICCD sensors in extremely low-light conditions. They often contains spatially clustered noises and general filtering methods do not work well. We find that the scale of the clustered noise in ICCD sensing images is often much smaller than that of the true structural information. Then the clustered noise can be identified by properly down-sampling and then up-sampling the ICCD sensing image and comparing it to the noisy image. Based on this finding, we present a denoising algorithm to remove the randomly clustered noise in ICCD images. First, we over-segment the ICCD image into a set of flat patches, and each patch contains very little structural information. Second, we classify the patches into noisy patches and noise-free patches based on the hypergraph cut method. Then the noise-free patches are easily recovered by the general block-matching and 3D filtering (BM3D) algorithm, since they often do not contain the clustered noise. The noisy patches are recovered by subtracting the identified clustered noise from the noisy patches. After that, we could get the whole recovered ICCD image. Finally, the quality of the recovered ICCD image is further improved by diminishing the remaining sparse noise with robust principal component analysis. Experiments are conducted on a set of ICCD images and compared with four existing denoising algorithms, which shows that the proposed algorithm removes well the randomly clustered noise and preserves the true textural information in the ICCD sensing images.
增强型电荷耦合器件（ICCD）图像是由ICCD传感器在极低光照条件下捕获的。它们通常包含空间聚类噪声，一般的滤波方法效果不佳。我们发现ICCD传感图像中聚类噪声的尺度通常比真实结构信息的尺度小得多。然后，可以通过对ICCD传感图像进行适当的下采样再上采样，并将其与有噪声图像进行比较来识别聚类噪声。基于这一发现，我们提出了一种去噪算法来去除ICCD图像中的随机聚类噪声。首先，我们将ICCD图像过分割成一组平坦的小块，每个小块包含的结构信息很少。其次，我们基于超图割方法将小块分类为有噪声小块和无噪声小块。然后，由于无噪声小块通常不包含聚类噪声，通过一般的块匹配和三维滤波（BM3D）算法很容易恢复它们。通过从有噪声小块中减去识别出的聚类噪声来恢复有噪声小块。之后，我们可以得到整个恢复后的ICCD图像。最后，通过用鲁棒主成分分析减少剩余的稀疏噪声，进一步提高恢复后的ICCD图像的质量。在一组ICCD图像上进行了实验，并与四种现有的去噪算法进行了比较，结果表明所提出的算法能很好地去除随机聚类噪声，并保留ICCD传感图像中的真实纹理信息。
