CAJ | 학술논문

为解决图像降噪过程中的弱边缘问题，提出了一种基于上下文量化和局部线性回归的医学图像降噪方法。应用信息论中上下文量化技术将复杂的混合噪声模型转换成一个局部回归分析问题，并设计了一个基于上下文的自适应滤波器；应用自适应核回归分析，进一步解决了滤波器的参数高鲁棒性估计问题；结合基于上下文的自适应滤波器技术的图像增强方法，研究了一个有效的多尺度医学图像增强算法系统。对该医学图像增强技术进行系统评估后表明，基于上下文量化的多尺度医学图像增强技术优于现有算法。
위해결도상강조과정중적약변연문제，제출료일충기우상하문양화화국부선성회귀적의학도상강조방법。응용신식론중상하문양화기술장복잡적혼합조성모형전환성일개국부회귀분석문제，병설계료일개기우상하문적자괄응려파기；응용자괄응핵회귀분석，진일보해결료려파기적삼수고로봉성고계문제；결합기우상하문적자괄응려파기기술적도상증강방법，연구료일개유효적다척도의학도상증강산법계통。대해의학도상증강기술진행계통평고후표명，기우상하문양화적다척도의학도상증강기술우우현유산법。
To deal with the problem of weak edges in medical image algorithm using context quantization and local linear regression. Context according to minimization of conditional entropy of the GAP prediction cells and the local texture features hidden in the context. Local signing a robust filter for pixels in each quantized context. Ex proposed algorithm outperforms conventional edge-preserving fil denoising, we present an quantization is conducted residual in the quantized linear regression is applied in de-perimental results show that the ters reviewed in this work.