基于人工神经网络的光学图像标准化显示研究

A Neural Network Processing Method Based on Self-assembly Equipment for Optical Image Display Standardization

光学图像作为二维或三维数据集,在地理、医学和遥感图像等领域是一种重要的通讯数据,因此,对光学图像的正确表达是进行数据通讯和分析的关键。基于医学图像标准DICOM中定义的灰度标准显示函数(GSDF),本文提出了一种光学图像显示方法。与DICOM不同的是,本方法采用人工神经网络获取GSDF模型,所有实验数据来自中国计量科学研究院自建的光学图像显示装置,该装置具备可溯源性,从而保证了该方法普遍适用于各类光学图像的标准化显示。

Optical image is a kind of important data for communication because it is two or three-dimension data set to express communication information such as geographical signal,medical signal,remote sensing signal,etc.Thus,how to express the optical image properly is critical for the communication analytics.A new display method for optical image is described which is derived from the concept-Grayscale Standard Display Function(GSDF)which has been defined in DICOM,a medical image standard.The method analysis GSDF based on neural network processing which is different to DICOM.And the training data are from a self-assembly Equipment in NIM which is a traceable optical display equipment.Thus,the method has common usage for all optical image display,exceeding medical image.Furthermore,it is suitable for standardization because of the traceability.