The invention discloses a CT image denoising method based on wavelet transformation, belongs to the technical field of medical image processing, and is particularly suitable for CT image denoising of new crown pneumonia. The CT image is susceptible to the interference of Gaussian noise in the transmission and acquisition process, and the wavelet transform can effectively remove the interference of the Gaussian noise. In order to solve the problems that early-stage lesions of a new crown CT image are not obvious in change, the number of the lesions is small, the range of the lesions is small, the density is low, and missed diagnosis of early-stage new crown patients is easily caused, the contrast ratio of the new crown lesions is improved, namely, an arc tangent improved self-adaptive wavelet threshold function of index adjustment and an improved threshold based on contraction factors are provided; the arc tangent function changes quickly near the zero point and changes slowly away from the zero point, the exponential function is adjusted to adapt to different layer threshold functions, more high-frequency detail information in the lung CT image is obtained, the detail edge is reserved, and fuzziness is reduced. The selection of wavelet threshold function parameters is a key factor for determining distortion and errors after image denoising, and the optimal adjustment parameters are searched through the improved particle swarm optimization of sine and cosine fusion normal distribution, so that the threshold optimization effect is greatly improved.