扩张血管的直径和深度是鲜红斑痣(PWS)扩张血管的两个关键参数。最近，光学相干层析(OCT)已被用于PWS的临床检测和参数提取。然而，信号在深度上的衰减和散斑噪声是分辨OCT图像中PWS病变结构信息的两个主要制约因素。为了优化临床OCT样机获得的图像，提出了相应的OCT信号增强降斑算法。该算法基于动态规划分割PWS表皮层，并以边界曲线为基准，对真皮深层细节信息进行衰减补偿。对于增强后的信号，基于OCT散斑噪声的光学统计特性(瑞利分布)，建立了一个新的正则化变分模型以消除相应的乘性噪声。实验结果表明，所提出的算法能够较好地恢复OCT图像中PWS扩张血管结构的边缘信息，有利于精确分割并提取相应的关键参数。

Diameter and depth of dilated blood vessels are two key parameters of port wine stain (PWS) lesions. Recently, optical coherence tomography (OCT) has demonstrated considerable promise for clinical test and the key parameters extraction of PWS. However, intensity attenuation in depth and speckle noise in OCT images constitute two primary limiting factors with respect to resolving the morphologic information of the PWS lesions. In order to enhance the visual quality of the OCT images, we develop new image speckle reduction algorithms for the OCT signal. In the study, epidermis segmentation is based on a dynamic programming scheme; according to the epidermal boundary curve as a baseline, the details in the deep are enhanced by the attenuation compensation. The visual inspection of the enhanced images is then improved by a new variation model that combines the regularization term with the statistical characteristic constraints (Rayleigh distribution) of data corrupted by OCT speckle noise to eliminate the corresponding multiplicative noise. The result shows that the proposed algorithm provides significant improvements of the edge information about the obtained PWS OCT images of dilated blood vessels, which is helpful to divide and extract the key parameters.