An improved denoising algorithm based on multi-scale dyadic wavelet transform

The presence of random noise in a CT system degrades the quality of CT images and therefore poses great difficulty to following tasks, such as segmentation and signal identification. In this paper, an efficient denoising algorithm is proposed to improve the quality of CT images. This algorithm mainly consists of three steps: (1) According to the inter-scale relationship of wavelet coefficient magnitude sum in the cone of influence (COI), wavelet coefficients are classified into two categories: edge-related and regular coefficients and irregular coefficients; (2) For edge-related and regular coefficients, only those located at the lowest decomposition level are denoised by wiener filtering, while no changes are made on coefficients located at other decomposition levels. (3) Irregular coefficients are denoised at all levels by wiener filtering. This algorithm is performed on projection data from which CT images are reconstructed. Experimental results show that: (1) It can effectively reduce the noise intensity while preserving the information of details as much as possible; (2) It is independent of CT scanning geometry and thus applicable to various CT systems. The denoising results indicate that this algorithm can offer great help to follow-up analysis based on CT images.