Texture smoothing is a fundamental tool in various applications. In this work, a new image texture smoothing method is proposed by defining a novel objective function, which is optimized by L0-norm minimization and a ...Texture smoothing is a fundamental tool in various applications. In this work, a new image texture smoothing method is proposed by defining a novel objective function, which is optimized by L0-norm minimization and a modified relative total variation measure. In addition, the gradient constraint is adopted in objective function to eliminate the staircase effect, which can preserve the structure edges of small gradients. The experimental results show that compared with the state-of-the-art methods, especially the L0 gradient minimization method and the relative total variation method, the proposed method achieves better results in image texture smoothing and significant structure preserving.展开更多
This paper proposes a structure-aware nonlocal energy optimization framework for interactive image colo- rization with sparse scribbles. Our colorization technique propagates colors to both local intensity-continuous ...This paper proposes a structure-aware nonlocal energy optimization framework for interactive image colo- rization with sparse scribbles. Our colorization technique propagates colors to both local intensity-continuous regions and remote texture-similar regions without explicit image segmentation. We implement the nonlocal principle by computing k nearest neighbors in the high-dimensional feature space. The feature space contains not only image coordinates and intensities but also statistical texture features obtained with the direction-aligned Gabor wavelet filter. Structure maps are utilized to scale texture features to avoid artifacts along high-contrast boundaries. We show various experimental results and comparisons on image colorization, selective recoloring and decoloring, and progressive color editing to demonstrate the effectiveness of the proposed approach.展开更多
基金Supported by the National Natural Science Foundation of China Youth Fund(No.61807029)Natural Science Foundation of Hebei Province(No.F2019203427).
文摘Texture smoothing is a fundamental tool in various applications. In this work, a new image texture smoothing method is proposed by defining a novel objective function, which is optimized by L0-norm minimization and a modified relative total variation measure. In addition, the gradient constraint is adopted in objective function to eliminate the staircase effect, which can preserve the structure edges of small gradients. The experimental results show that compared with the state-of-the-art methods, especially the L0 gradient minimization method and the relative total variation method, the proposed method achieves better results in image texture smoothing and significant structure preserving.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61100146 and 61472351, and the Zhejiang Provincial Natural Science Foundation of China under Grant Nos. LY15F020019 and LQ14F020006. Pan was supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant No. 2013BAH24F01. Acknowledgement CVM 2015 anonymous We would like to thank our reviewers for their constructive and helpful comments which definitely improve ttle quality of the paper.
文摘This paper proposes a structure-aware nonlocal energy optimization framework for interactive image colo- rization with sparse scribbles. Our colorization technique propagates colors to both local intensity-continuous regions and remote texture-similar regions without explicit image segmentation. We implement the nonlocal principle by computing k nearest neighbors in the high-dimensional feature space. The feature space contains not only image coordinates and intensities but also statistical texture features obtained with the direction-aligned Gabor wavelet filter. Structure maps are utilized to scale texture features to avoid artifacts along high-contrast boundaries. We show various experimental results and comparisons on image colorization, selective recoloring and decoloring, and progressive color editing to demonstrate the effectiveness of the proposed approach.
