Zhang Xuming, Li Liu, Zhu Fei, Hou Wenguang, Chen Xinjian
Huazhong University of Science and Technology, School of Life Science and Technology, 1037 Luoyu Road, Wuhan 430074, China.
Soochow University, School of Electronics and Information, 1 Shizi Street, Suzhou 215006, China.
J Biomed Opt. 2014 Jun;19(6):066005. doi: 10.1117/1.JBO.19.6.066005.
Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.
光学相干断层扫描（OCT）图像通常会因严重的散斑噪声而退化，这将严重阻碍其定量分析。然而，由于难以区分噪声和散斑图案的信息成分，OCT图像中的散斑噪声减少尤其具有挑战性。为了解决这个问题，提出了基于脉冲皮质模型（SCM）的非局部均值方法。该方法基于SCM提取的图像块的旋转不变特征探索OCT图像的自相似性，然后通过对相似块进行平均来恢复有斑点的图像。由于该方法在高散斑噪声污染下能有效找到可靠的相似块，因此在很好地保留图像细节的同时能提供足够的散斑减少。当应用于视网膜OCT图像时，该方法能将信噪比提高>16 dB，相似度损失仅为5.4%。
