Contrast-preserving image smoothing via the truncated first-order rational function

Abstract

The main task of image smoothing is to remove the insignificant details of the input image while preserving salient structural edges. In the fields of computer vision and graphics, image smoothing techniques are of great practical importance. In this paper, we investigate a new nonconvex variational optimization model for contrast-preserving image smoothing based on the truncated first-order rational (TFOR) penalty function. We employ an iterative numerical method that utilizes the half-quadratic minimization to effectively solve the proposed model. To validate the effectiveness of the proposed method, we compare it with some related state-of-the-art methods. Experimental results are given to show that the proposed method performs well in preserving the image contrast while maintaining the important edges and structures. We apply the proposed method on various classic image processing tasks such as clip-art compression artifact removal, detail enhancement, image denoising, image abstraction, flash and no-flash image restoration, and guided depth map upsampling.