Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO

•In section 2, an improved wavelet threshold based on the shrinkage factor is proposed. In this method, the threshold decreases with the increase of decomposition scale. It improves the accuracy of noise detection to a greater extent for asymptomatic COVID-19.•In section 2, we develop the wavelet threshold function based on the adjustment factor integrated with the arc tangent, which overcomes the discontinuity and the constant deviation of the traditional threshold function. It is suitable for noisy signals with different variance.•In section 3, a wavelet transform based on the optimization of parameters combined with improved PSO is proposed, so that the wavelet parameters can change adaptively according to the details of lung lobes and ground-glass shadow with relatively few iterations.•In section 4, aiming at the different kinds of asymptomatic COVID-19 CT images, the simulation experiments prove that the paper method has strong robustness to Gaussian noise, which enhances the ability of image denoising while better protecting the details of the lesion. It reduces the rate of missed and mistake diagnosis for asymptomatic COVID-1.