Denoising Technology for Radiation Image with Poisson Noise Based on Shearlet Transform

In order to reduce the radiation image noise caused by statistical fluctuation, a denoising method based on shearlet transform was proposed. The radiation image of lowdose radiation or object with large mass thickness was taken as research objects. Through noise analysis, Anscombe transform was used to convert Poisson noise caused by statistical fluctuation into Gaussian noise, then shearlet decomposition, threshold denoising, shearlet reconstruction and Anscombe inverse transform were utilized to obtain the denoised image. The results show that the optimal denoising effect can be achieved when the scale of shearlet decomposition is 5 and the improved thresholding and the threshold of minimax principle are chosen. This method can reduce Poisson noise and retain image details. Moreover, it is superior to the traditional methods in both visual feeling and quantitative parameter.