Denoising method for X-ray images with poisson-Gaussian noise based on a new threshold function and shearlet transform

Abstract

Background

X-ray imaging has been applied in various fields. However, the noise in X-ray images reduces the image quality and affects subsequent detection.

Aims

A denoising method is developed to solve the problem of the Poisson-Gaussian mixed noise caused by X-ray imaging.

Method

ology: A new threshold function and an improved threshold are proposed in this paper. Furthermore, an improved denoising method, namely the improved generalized Anscombe with shearlet transform (improved GA-ST), is developed based on the above proposed algorithms. After theoretical derivation, experiments and parameter analysis, the proposed method is applied to actual X-ray images.

Results

The results show that the new threshold function is continuous, asymptotic, and has no inherent deviation, which solves the problems existing in traditional threshold functions. In addition, the improved GA-ST method can reduce Poisson-Gaussian mixed noise at different levels. As for actual X-ray images, the improved GA-ST method outperforms the other methods, and the BRISQUE descent ratios all exceed 25%.

Conclusions

The improved GA-ST method proposed in this paper can effectively reduce the noise in X-ray images and meet the requirements of actual applications based on MATLAB platform.