Aimed at the problems that the existing denoising methods suppress noise incompletely and blur the details of image, an image denoising method using kernel fuzzy C-means clustering and regularization on sparse model was proposed. Firstly, the image was divided into equal pieces and kernel fuzzy C-means clustering algorithm was used for clustering the similar image pieces, thereby ensuring image pieces in the same class share the same sparse denoising model. Then, the global dictionary trained by images from the classical image library was selected as the initial dictionary to adapt to the various characteristics of image very well. Next, a 1/2 norm regularization constraint condition was imposed and sparse decomposition of image pieces in the same class under the dictionary was achieved, which made decomposition coefficients sparser. Finally, the update of dictionary was completed by improved K-singular value decomposition algorithm, and image pieces with the largest difference from the original sparse model were selected to replace the redundancy atoms of the uapdated dictionary. Thus, noise in the image was suppressed effectively. Experimental results show that, compared with denoising method based on wavelet combining with nonlinear diffusion, denoising method based on constant dictionary, denoising method of optimal directions and K-singular value decomposition denoising method, the proposed method can remove noise of the image more effectively and preserve the details of the image and improve the visual effect better.