Department of Brain and Cognitive Engineering, Korea University, 5-1, Anam-Dong, Seoul, 136-713, South Korea.
Opt Lett. 2011 Aug 15;36(16):3212-4. doi: 10.1364/OL.36.003212.
We present an iterative method of eliminating pixelation artifacts from an endoscopic image acquired from a coherent fiber bundle imager. Our proposed approach for decoupling the honeycomb effect from the obtained sample image was formulated by using the prior probability for an approximate Bayesian framework in which the ideal complete image can be estimated by maximizing the posterior probability from the observed image. The maximization of the posterior probability from the original mask image (the mirrored fiber bundle imager structure) and the observed image (the sample image of the United States Air Force chart) has been performed by learning the image priors in the space of Markov random fields. By iteratively estimating the probability distribution, we reduced the noise effects from the mask image and recovered the ideal shape of the image. This method was efficient for automatically learning the sliding patch from the combination of projected kernels. The mask and observed images were obtained from en face images of the Fourier domain optical coherence tomography based on a common path interferometry scheme.
我们提出了一种从相干光纤束成像仪获取的内窥镜图像中消除像素化伪影的迭代方法。我们提出的从获得的样本图像中分离蜂窝状效应的方法是通过使用近似贝叶斯框架中的先验概率来制定的，其中可以通过最大化从观察到的图像的后验概率来估计理想的完整图像。通过在马尔可夫随机场的空间中学习图像先验，可以对原始掩模图像（镜像光纤束成像仪结构）和观察到的图像（美国空军图表的样本图像）进行最大化后验概率的操作。通过迭代地估计概率分布，我们减少了掩模图像的噪声影响，并恢复了图像的理想形状。这种方法对于从投影核的组合中自动学习滑动补丁非常有效。掩模和观察图像是从基于共路干涉测量方案的傅里叶域光学相干断层扫描的正面对图像中获得的。
