基于RCSCT变换的DR图像去噪及加速

目的 数字化X线摄影（digital radiography,DR）图像中的高斯噪声对图像质量影响大,消除此类噪声有利于提高图像质量以辅助医生做出正确的诊断.方法 为抑制DR图像的高斯噪声,首先采用递归循环平移与Contourlet变换结合的（recursive cycle spinning Contourlet transform,RCSCT）方法变换分解DR图像,接着采用连续的二元软阈值函数处理变换系数防止系数被过度扼杀,然后基于CUDA（compute unified device architecture,计算统一设备架构）平台对去噪方法加速.结果 该方法提高了去噪后的图像峰值信噪比,有效抑制了伪吉布斯现象,保留了更多的图像细节信息,并且加速处理后运算耗时较短.结论 本文方法比小波变换和Contourlet变换在保留视觉细节信息方面效果更优,算法耗时少,实用性好.

DR image de-noising and acceleration based on recursive cycle pinning contourlet transformation

Objective The influence of Gaussian noise on digital radiography（DR） image is great, and the removal of Gaussian noise is beneficial to the image quality and clinical diagnosis. Methods To suppress Gaussian noise of DR image,this paper first decomposes DR image based on recursive cycle spinning Contourlet transform （RCSCT）that combines recursive cycle spinning and Contourlet transform, then adopts continuous binary soft threshold function to process the transformed coefficients, which can prevent coefficients over killed, and subsequently accelerates the de-noising method based on compute unified device architecture （CUDA） platform. Results The experimental results show that the suggested method can obtain higher PSNR value, inhibit Gibbs-like phenomena, and preserve more image details with shorter time-consuming after acceleration. Conclusions The proposed method based on RCSCT is better than wavelet transform and Contourlet transform in practicability, time consuming and preservation of visual information.