The contrast enhancement of blurred and noised image is discussed. It constructs an edge-directed anisotropic nonlinear diffusion to constrain the smoothness of the image,and constructs an unhomogeneous approach constraint according to the distribution of the imaging error. A regularization model for contrast enhancement is obtained by combining these two constraints through a regularization parameter. This regularization model can turn to be an anisotropic nonlinear diffusion by solving its Euler equation,which can be solved fast. The new model can reduce noise and enhance edge at the same time,and can avoid the computation of huge matrix. Numerical results showed that the new model was suited for different blur and a wide range of noise levels,and could get sharper edges and clearer images than known methods,and it also had one or two dB higher peak signal to noise ratio(PSNR)and higher edge kept index(EKI)than known models.