An image denoising method, comprising: acquiring image data of an image; performing wavelet decomposition on at least one component of three components of the image data: a luminance component y and chrominance components u and v, to obtain a high frequency wavelet coefficient and a low frequency wavelet coefficient of each component; performing recursive denoising in at least one direction on the low frequency wavelet coefficient of each component, to obtain a denoised low frequency wavelet coefficient of each component according to: c k d = c k + f c k − 1 d − c k wherein c k d is a result of denoising of the k th low frequency wavelet coefficient, c k is a value of the k th low frequency wavelet coefficient, k > 1, and k is an integer, f is a function determined by the estimated noise level of the image data; performing wavelet reconstruction according to the high frequency wavelet coefficient of each component and the denoised low frequency wavelet coefficient of each component, to obtain at least one denoised component; and when the at least one denoised component is three components, combining the three denoised components to obtain denoised image data; when the at least one denoised component is one or two components, combining the at least one denoised component with the other one or two components of the three components to obtain denoised image data. acquiring image data of an image; performing wavelet decomposition on at least one component of three components of the image data: a luminance component y and chrominance components u and v, to obtain a high frequency wavelet coefficient and a low frequency wavelet coefficient of each component; performing recursive denoising in at least one direction on the low frequency wavelet coefficient of each component, to obtain a denoised low frequency wavelet coefficient of each component according to: c k d = c k + f c k − 1 d − c k wherein c k d is a result of denoising of the k th low frequency wavelet coefficient, c k is a value of the k th low frequency wavelet coefficient, k > 1, and k is an integer, f is a function determined by the estimated noise level of the image data; performing wavelet reconstruction according to the high frequency wavelet coefficient of each component and the denoised low frequency wavelet coefficient of each component, to obtain at least one denoised component; and when the at least one denoised component is three components, combining the three denoised components to obtain denoised image data; when the at least one denoised component is one or two components, combining the at least one denoised component with the other one or two components of the three components to obtain denoised image data. wherein c k d is a result of denoising of the k th low frequency wavelet coefficient, c k is a value of the k th low frequency wavelet coefficient, k > 1, and k is an integer, f is a function determined by the estimated noise level of the image data; performing wavelet reconstruction according to the high frequency wavelet coefficient of each component and the denoised low frequency wavelet coefficient of each component, to obtain at least one denoised component; and when the at least one denoised component is three components, combining the three denoised components to obtain denoised image data; when the at least one denoised component is one or two components, combining the at least one denoised component with the other one or two components of the three components to obtain denoised image data.