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首页> 外文会议>Conference on Optical Coherence tomography and coherence domain optical methods in biomedicine XXI >A Stochastically Fully Connected Conditional Random Field Framework for Super Resolution OCT
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A Stochastically Fully Connected Conditional Random Field Framework for Super Resolution OCT

机译：用于10月超级分辨率的随机完全连接的条件随机场框架

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A number of factors can degrade the resolution and contrast of OCT images, such as: (1) changes of the OCT point-spread function (PSF) resulting from wavelength dependent scattering and absorption of light along the imaging depth (2) speckle noise, as well as (3) motion artifacts. We propose a new Super Resolution OCT (SR OCT) imaging framework that takes advantage of a Stochastically Fully Connected Conditional Random Field (SF-CRF) model to generate a Super Resolved OCT (SR OCT) image of higher quality from a set of Low-Resolution OCT (LR OCT) images. The proposed SF-CRF SR OCT imaging is able to simultaneously compensate for all of the factors mentioned above, that degrade the OCT image quality, using a unified computational framework. The proposed SF-CRF SR OCT imaging framework was tested on a set of simulated LR human retinal OCT images generated from a high resolution, high contrast retinal image, and on a set of in-vivo, high resolution, high contrast rat retinal OCT images. The reconstructed SR OCT images show considerably higher spatial resolution, less speckle noise and higher contrast compared to other tested methods. Visual assessment of the results demonstrated the usefulness of the proposed approach in better preservation of fine details and structures of the imaged sample, retaining biological tissue boundaries while reducing speckle noise using a unified computational framework. Quantitative evaluation using both Contrast to Noise Ratio (CNR) and Edge Preservation (EP) parameter also showed superior performance of the proposed SF-CRF SR OCT approach compared to other image processing approaches.
机译：许多因素可以降解OCT图像的分辨率和对比，例如：（1）由波长依赖性散射和沿着成像深度（2）斑点噪声的光的吸收产生的OCT点扩展功能（PSF）的变化，以及（3）运动伪影。我们提出了一个新的超分辨率OCT（SR OCT）成像框架，该框架利用了一个随机完全连接的条件随机场（SF-CRF）模型来生成从一组低的高质量的超分辨率OCT（SR OCT）图像十月分辨率（LR OCT）图像。所提出的SF-CRF SR OCT成像能够同时补偿上述所有因素，从而利用统一的计算框架来降低OCT图像质量。所提出的SF-CRF SR OCT成像框架在一组模拟的LR人类视网膜OCT图像上进行了测试，从高分辨率，高对比度视网膜图像和一组体内，高分辨率，高对比度大鼠视网膜OCT图像上产生。与其他测试方法相比，重建的SR OCT图像显示出相当高的空间分辨率，较少的散斑噪声和更高的对比度。对结果的视觉评估表明了提出的方法的有用性，以更好地保存成像样品的细节和结构，保持生物组织边界，同时使用统一的计算框架减少斑点噪声。与其他图像处理方法相比，使用与噪声比（CNR）和边缘保存（EP）参数的对比度（CNR）和边缘保存（EP）参数的定量评估也显示出所提出的SF-CRF SR OCT方法的优越性。

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