Edge-preserving smoothing algorithm based on <italic>l</italic><sub>0</sub> gradient minimization of image-patch

Abstract

Image smoothing algorithm based on l₀ gradient minimization can smooth details and textures of image while preserving edges. Since the algorithm uses image gradient to determine the smoothed component, the region with smaller image gradients (weak edge) can be smoothed. However, the region with larger image gradients (strong edge) will be preserved. In order to overcome this drawback, we propose an image-patch based l₀ gradient minimization image smoothing algorithm (IP-<italic>l</italic>₀). Instead of globally smoothing the input image, our algorithm smoothed each image patch first and then combined the smoothed patches together to obtain the final smoothed image. The weight parameters in the objective function used to smooth each image patch are dynamically changed according to the local statistics of image patch, so that the patches containing strong textures will be smoothed with greater force, and vice versa. The experimental results show that compared to the original l₀ gradient minimization algorithm and several other state-of-the-art edge-preserving image smoothing algorithms including the local Laplacian filter based algorithm, the relative total variation based algorithm, the tree filter based algorithm, and two kinds of deep-learning based smoothing algorithms, the proposed algorithm can effectively smooth strong textures and well preserve weak edges or structures, and the ability of edge preservation and texture smoothing is better than other algorithms.