Image detail feature and noise mainly focus on high frequency part of image

which will make some important features of image (such as edge and fine texture) broken during image denoising. Aimed at such problems

a kind of wavelet transform image denoising method based on curvature variation regularization is set forth in this paper. Firstly

the wavelet is used to extract the high frequency part of image. Secondly

the wavelet is used to make enhancement process for images to get the enhanced image. Because the level set curvature is an important description for its morphological characters

the level set curvature of enhanced image is used to establish a curvature-driven function. Then

the curvature-driven function will be introduced to a variation model as a correction factor to establish a curvature variation model which controls the whole structure of image. This model overcomes the error diffusion caused by lack of image gradient information in ROF model and also conforms to the morphological principle of image processing. Finally

the established curvature variation model is used to process the extracted high frequency part

the wavelet reconstruction is carried out with the processed high frequency coefficients and original low frequency coefficients to get the denoised image. The analysis and simulation indicates that the method can restrict the noise in an effective way and a high similarity of image structure can be got. So a superior denoising effect can be achieved.