Abstract
The impulse noise in CT image was removed based on edge-preserving median filter algorithm. The sparse nonlocal regularization algorithm weighted coding was used to remove the impulse noise and Gaussian noise in the mixed noise, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) were calculated to evaluate the quality of the denoised CT image. It was found that in nine different proportions of Gaussian noise and salt-and-pepper noise in Shepp-Logan image and CT image processing, the PSNR and SSIM values of the proposed denoising algorithm based on edge-preserving median filter (EP median filter) and weighted encoding with sparse nonlocal regularization (WESNR) were significantly higher than those of using EP median filter and WESNR alone. It was shown that the weighted coding algorithm based on edge-preserving median filtering and sparse nonlocal regularization had potential application value in low-dose CT image denoising.
Highlights
CT images are susceptible to the interference of quantum noise and electronic noise of detectors and other noises during the acquisition process, which causes the quality of reconstructed images to decrease, which in turn affects the diagnosis of diseases by doctors [1]
Jiang et al [10] proposed a denoising algorithm based on weighted coding and sparse nonlocal regularization (WESNR), which simultaneously removes impulse noise and Gaussian noise through soft impulse pixel detection. e principal component analysis (PCA) dictionary was used to encode image blocks, and the coding residuals were weighted to suppress the heavy tail of the distribution. e image sparse prior and nonlocal self-similarity prior were merged into a single nonlocal sparse regularization term, which enhances the stability of weighted coding
A denoising algorithm based on EP median filter and WESNR is proposed, which can remove the mixed noise of low-dose CT image and preserve the edge information of the image
Summary
CT images are susceptible to the interference of quantum noise and electronic noise of detectors and other noises during the acquisition process, which causes the quality of reconstructed images to decrease, which in turn affects the diagnosis of diseases by doctors [1]. Cui et al [9] proposed a learning method based on morphological component analysis, which can automatically generate an adaptive discriminant dictionary and effectively suppress the artifacts of low-dose CT images under the framework of sparse representation. E image sparse prior and nonlocal self-similarity prior were merged into a single nonlocal sparse regularization term, which enhances the stability of weighted coding When this method was applied to low-dose CT image denoising, details were lost, and edges were destroyed. The edge-preserving filtering algorithm is not effective for mixed noise and noise with low signal-to-noise ratio In this exploration, a denoising algorithm based on EP median filter and WESNR is proposed, which can remove the mixed noise of low-dose CT image and preserve the edge information of the image
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