光学相干层析扫描(OCT)作为一种新型无创高分辨率扫描方式, 在临床上得到广泛应用, 但是 OCT图像本身存在严重的散斑噪声, 这大大影响了疾病的诊断。本文针对 OCT图像中的乘性散斑噪声, 改进了两种原始字典降噪算法。该算法首先对 OCT图像进行对数变换, 采用正交匹配追踪算法进行稀疏编码, 以及 K奇异值分解学习算法进行自适应字典的更新, 最后通过加权平均以及指数变换回到空域。实验结果表明, 本文改进的两种字典算法能有效降低 OCT图像中的散斑噪声, 获得良好的视觉效果。并通过均方误差 (MSE)、峰值信噪比 (PSNR)、结构相似性 (SSIM)以及边缘保持指数(EPI)四个指标评价降噪效果, 与两种原始字典降噪算法和传统滤波算法相比, 两种改进字典算法降噪效果优于其他算法, 其中自适应字典算法表现更好。

As a new non-invasive and high-resolution scanning method, optical coherence tomography(OCT) has been widely used in clinical practice, but OCT images haveserious speckle noise, which greatlyaffects the diagno-sis of diseases. Two original dictionary noise reduction algorithms have been improved for multiplicative speckle noise in OCT. The algorithm first performs logarithmic transformation on OCT images, uses orthogonal matching pursuit algorithm for sparse coding, and K singular value decomposition learning algorithm to update adaptive dic-tionary. Finally, it returns to the space domain through weighted average and exponential transformation. The expe-rimental results show that the improved two dictionary algorithms can effectively reduce the speckle noise in OCT images and obtain good visual effects. The noise reduction effect is evaluated by four factors: mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and edge-preserving index (EPI). Compared with the two original dictionary noise reduction algorithms and the traditional filtering algorithms, the noise reduction effect of the two improved dictionary algorithms is better than that of other algorithms, and the improved adaptive dictionary algorithm performs better.