A hybrid method based CT image denoising using nonsubsampled contourlet and curvelet transforms

Abstract

Computed tomography (CT) is one of the most widespread radio-logical tools for diagnosis purpose. To achieve good quality of CT images with low radiation dose has drawn a lot of attention to researchers. Hence, post-processing of CT images has become a major concern in medical image processing. This paper presents a novel edge-preserving image denoising scheme where noisy CT images are denoised using nonsubsampled contourlet transform (NSCT) and curvelet transform separately. By estimating variance difference on both denoised images, final denoised CT image has been achieved using a variation-based weighted aggregation. The proposed scheme is compared with existing methods and it is observed that the performance of proposed method is superior to existing methods in terms of visual quality, image quality index (IQI), and peak signal-to-noise ratio (PSNR).