Smoothing and Clustering Guided Image Decolorization

Fang Li; Yuanming Zhu

doi:10.5566/ias.2348

Authors

Fang Li East China Normal University https://orcid.org/0000-0001-6804-2651
Yuanming Zhu East China Normal University

DOI:

https://doi.org/10.5566/ias.2348

Keywords:

clustering, contrast, decolorization, optimization, smoothing

Abstract

In this paper, we propose a new image decolorization method based on image clustering and weight optimization. First, we smooth the color image and cluster it into several classes and get the class centers. Each center can represent a distinctive color in the image. Then the class centers are sorted according to their brightness measured by Euclidean norm. By assuming that the decolorized grayscale image is a linear combination of the three channels of the color image, we propose an optimization problem by forcing the sorted class centers to correspond to specified grayscale values satisfying uniform distribution. Numerically, the problem is solved by quadratic programming. Experiments on two popular data sets demonstrate that the proposed method is competitive with the state-of-the-art decolorization method.

Author Biographies

Fang Li, East China Normal University

School of Mathematical Sciences
Yuanming Zhu, East China Normal University

School of Mathematical Sciences

References

Bala R, Eschbach R (2004). Spatial color-to-grayscale transform preserving chrominance edge information. In: Color and Imaging Conference, vol. 2004. Society for Imaging Science and Technology.

Bertalmio M, Caselles V, Provenzi E, Rizzi A (2007). Perceptual color correction through variational techniques. IEEE T IMAGE PROCESS 16:1058–72.

Bezdek JC, Ehrlich R, Full W (1984). FCM: The fuzzy c-means clustering algorithm. COMPUT GEOSCI 10:191–203.

Cadık M (2008). Perceptual evaluation of color-to-grayscale image conversions. In: COMPUT GRAPH FORUM, vol. 27. Wiley Online Library.

Du H, He S, Sheng B, Ma L, Lau RW(2015). Saliency-guided color-to-gray conversion using region-based optimization. IEEE T IMAGE PROCESS 24:434–43.

Gooch AA, Olsen SC, Tumblin J, Gooch B (2005). Color2gray: salience-preserving color removal. ACM T GRAPHIC 24:634–9.

Grundland M, Dodgson NA (2007). Decolorize: Fast, contrast enhancing, color to grayscale conversion. PATTERN RECOGN 40:2891–6.

Ham B, Cho M, Ponce J (2017). Robust guided image filtering using nonconvex potentials. IEEE T PATTERN ANAL 40:192–207.

Hou X, Duan J, Qiu G (2017). Deep feature consistent deep image transformations: Downscaling, decolorization and HDR tone mapping. arXiv preprint arXiv:17070.9482.

Ji Z, Fang Me, Wang Y, Ma W (2016). Efficient decolorization preserving dominant distinctions. The Visual Computer 32:1621–31.

Jin Z, Li F, Ng MK (2014). A variational approach for image decolorization by variance maximization. SIAM J IMAGING SCI 7:944–68.

Kim Y, Jang C, Demouth J, Lee S (2009). Robust color-to-gray via nonlinear global mapping. ACM T GRAPHIC 28:1–4.

Lau C, Heidrich W, Mantiuk R (2012). Cluster-based color space optimizations. In: IEEE I CONF COMP VIS.

Liu Q, Leung H (2018). Variable augmented neural network for decolorization and multi-exposure fusion. INFORM FUSION 46:114–27.

Liu Q, Li S, Xiong J, Qin B (2019). WpmDecolor: weighted projection maximum solver for contrast-preserving decolorization. VISUAL COMPUT

:205–21.

Liu Q, Liu P, Wang Y, Leung H (2017a). Semi-parametric decolorization with laplacian-based perceptual quality metric. IEEE T CIRC SYST

VID 27:1856–68.

Liu Q, Liu PX, Xie W, Wang Y, Liang D (2015). GcsDecolor: Gradient correlation similarity for efficient contrast preserving decolorization. IEEE T IMAGE PROCESS 24:2889–904.

Liu Q, Shao G, Wang Y, Gao J, Leung H (2017b). Log-euclidean metrics for contrast preserving decolorization. IEEE T IMAGE PROCESS

:5772–83.

Liu S, Zhang X (2019). Image decolorization combining local features and exposure features. IEEE T MULTIMEDIA 21:2461–72.

Lu C, Xu L, Jia J (2012a). Contrast preserving decolorization. In: IEEE INT CONF COMPUT. IEEE.

Lu C, Xu L, Jia J (2012b). Real-time contrast preserving decolorization. In: SIGGRAPH Asia 2012 Technical Briefs. ACM.

Lu C, Xu L, Jia J (2014). Contrast preserving decolorization with perception-based quality metrics. INT J COMPUT VISION 110:222–39.

Min D, Choi S, Lu J, Ham B, Sohn K, Do MN (2014). Fast global image smoothing based on weighted least squares. IEEE T IMAGE PROCESS 23:5638–53.

Neumann L, Cadık M, Nemcsics A (2007). An efficient perception-based adaptive color to gray transformation. In: Proceedings of the Third Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging. Eurographics Association.

Rasche K, Geist R, Westall J (2005). Detail preserving reproduction of color images for monochromats and dichromats. IEEE COMPUT GRAPH 25:22–30.

Smith K, Landes PE, Thollot J, Myszkowski K (2008). Apparent greyscale: A simple and fast conversion to perceptually accurate images and video. In: COMPUT GRAPH FORUM, vol. 27. Wiley Online Library.

Szilagyi L, Benyo Z, Szilagyi SM, Adam HS (2003). MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: P ANN INT IEEE EMBS, vol. 1.

Wang W, Li Z, Wu S (2018). Color contrast-preserving decolorization. IEEE T IMAGE PROCESS 27:5464–74.

Xu L, Lu C, Xu Y, Jia J (2011). Image smoothing via L0 gradient minimization. In: ACM T GRAPHIC, vol. 30. ACM.

Zhang X, Liu S (2018). Contrast preserving image decolorization combining global features and local semantic features. The Visual Computer 34:1099–108.

Zhao H, Zhang H, Jin X (2018). Efficient image decolorization with a multimodal contrast-preserving measure. COMPUT GRAPH 70:251–