Scale and Gradient Aware Image Smoothing

This paper presents a novel scale and gradient aware image smoothing method, particularly effective for removing high-contrast detailed textures while preserving boundary sharpness and fine structures. The core idea of the proposed method is based on an observation that small-scale textures can be removed only depending on a down-then-up scaling(DTUS) operation. Accordingly, we present a selective edge smoothing framework by jointly considering scale and gradient measurements. Specifically, we first compute the structural similarity(SSIM) of the DTUS image and the input image to distinguish the textures and structures from the input image. Then we use the SSIM map as weights to fuse the two images together to achieve a scale aware smoothing result. Furthermore, we use the fusion image as guidance to confine the number of non-zero gradients and perform a guided L0 gradient minimization to achieve gradient aware smoothing. Since our method makes full use of image scale and gradient, it outperforms state-of-the-art image smoothing algorithm, especially in removing high-contrast textures. Since our proposed method can remove the insignificant details while preserving sharp and undistorted structural edges, it is also adaptable to many application scenarios, such as boundary extraction, image abstraction, JPEG artifact removal, and layer decomposition-based editing.