To achieve an improved edge-preserving effect in the image smoothing process

a novel image smoothing algorithm using the sparse feature of pixel intensity and gradient as dual constraints is proposed. A pixel intensity and gradient function based on L0 norm is set as a constraint term of the smoothing model. Two auxiliary variables are introduced through half-quadratic splitting strategy to construct the final smoothing models. Finally

the alternating minimization algorithm is applied to solve the model

and a closed-form solution of the smoothed image is obtained in Fourier frequency to accelerate the speed of the algorithm. Smoothing experiments on natural images show that the proposed algorithm can better meet the requirements of edge preserving

denoising effects

and real-time applications; the proposed algorithm requires only 3.42 s and is 7.85 s faster than the bilateral filtering algorithm. The experiments demonstrate that the proposed algorithm outperforms other smoothing algorithms. The algorithm can remove unimportant details

retain the image edge features in the image

and achieve the effect of image smoothing. Thus

it is applicable to smoothing

denoising

and boundary enhancement of images with a complex background.