A two-stage parametric subspace model for efficient contrast-preserving decolorization

Hong-yang LU; Qie-gen LIU; Yu-hao WANG; Xiao-hua DENG

doi:10.1631/FITEE.1600017

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A two-stage parametric subspace model for efficient contrast-preserving decolorization

Regular Papers | Updated：2022-05-19

- A two-stage parametric subspace model for efficient contrast-preserving decolorization
- 一种基于两步参数子空间模型的对比度保持彩色图像灰度化算法
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 11, Pages: 1874-1882(2017)
- Affiliations：
  
  Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
  Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China
- Author bio：
  
  Qie-gen LIU, E-mail: liuqiegen@ncu.edu.cn
- Funds：
- DOI：10.1631/FITEE.1600017
  CLC： TM346
- Received：12 January 2016，
  
  Revised：2017-11-26，
  
  Published：2017-11
- Accepted：
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Hong-yang LU, Qie-gen LIU, Yu-hao WANG, et al. A two-stage parametric subspace model for efficient contrast-preserving decolorization[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1874-1882.
DOI：

Hong-yang LU, Qie-gen LIU, Yu-hao WANG, et al. A two-stage parametric subspace model for efficient contrast-preserving decolorization[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1874-1882. DOI： 10.1631/FITEE.1600017.

摘要

rgb2gray转换模型是目前最为经典和流行的彩色图像灰度化方法。最新的研究表明，对一阶线性模型的三个加权参数进行自适应离散搜索具有良好的潜力。在本文中，我们提出一种两步法方式，将参数搜索策略扩充到二阶多变量多项式模型。该模型划分为三个子空间的和，本文论证了第一个子空间是最为重要的，并且第二个子空间可以看作为对其进行加细的部分。在模型的第一步，将梯度相似性测度（gradient correlation similarity

Gcs）用于第一个子空间，得到一个初步的灰度化图像。然后，再次使用Gcs对初步灰度图像和第二子空间所引出形成的候选图像进行最优解搜索。通过数值实验，在定量评价、定性视觉评价和算法复杂度方面表明了本方法的有效性。

Abstract

The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete searching solver has a great potential in its conversion ability. In this paper

we present a two-step strategy to efficiently extend the parameter searching solver to a two-order multivariance polynomial model

as a sum of three subspaces. We show that the first subspace in the two-order model is the most important and the second one can be seen as a refinement. In the first stage of our model

the gradient correlation similarity (Gcs) measure is used on the first subspace to obtain an immediate grayed image. Then

Gcs is applied again to select the optimal result from the immediate grayed image plus the second subspace-induced candidate images. Experimental results show the advantages of the proposed approach in terms of quantitative evaluation

qualitative evaluation

and algorithm complexity.

关键词

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references

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