基于场景传输渐进估计的单幅图像能见度快速复原

廖斌; 訚鹏; 赵俭辉

doi:10.11999/JEIT150515

基于场景传输渐进估计的单幅图像能见度快速复原

doi: 10.11999/JEIT150515

2.
(湖北大学计算机与信息工程学院武汉 430062) ②(武汉大学计算机学院武汉 430072)

基金项目:

国家自然科学基金 (61300125)

计量
- 文章访问数: 1213
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- PDF下载量: 455
- 被引次数: 0
出版历程
- 收稿日期: 2015-05-04
- 修回日期: 2015-08-28
- 刊出日期: 2016-01-19

Fast Visibility Restoration of Single Image by Progressive Scene Transmission Estimation

1.
(School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China)
2.
(School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China)

Funds:

The National Natural Science Foundation of China (61300125)

摘要

摘要: 该文提出一种基于场景传输渐进估计的单幅图像能见度快速复原方法。利用场景辐射的边界条件，结合大气散射模型估计场景传输的初始值，并采用递归双边滤波对其精化。通过梯度域递归双边滤波分解复原结果，对得到的基层和细节层分别进行色调调整和增强，获取高质量的清晰图像。利用高斯KD树对原始图像的高维特征空间进行自适应剖分，获取其高斯采样以加速复原计算。与相关工作比较，所提方法能够保持边缘和细节，有效避免光晕效应。采用客观指标评价复原结果。实验结果表明，该方法有效易行，复原结果更加符合真实场景。
- 图像处理 /
- 能见度复原 /
- 递归双边滤波 /
- 自适应采样
Abstract: A fast visibility restoration method of single image by progressive scene transmission estimation is proposed. Based on the boundary conditions of scene radiance and the atmosphere scattering model, the initial value of the scene transmission is estimated and refined with recursive bilateral filtering. The restoration result is decomposed into a base layer and a detail layer by gradient domain recursive bilateral filtering. The high visibility?restoration result is obtained with tone mapping of the base layer and detail enhancement. Utilizing Gaussian KD tree, the high dimensional feature space of the original image is subdivided adaptively to obtain the Gaussian sampling and accelerate the restoration computing. Compared with related work, the proposed method preserves edges and details, avoiding the halo effectively. The objective indicators are applied to evaluate the restoration results. The experiment results show that the proposed method is effective and feasible, and the restoration results accord with the real scene better.
- Image processing /
- Visibility restoration /
- Recursive bilateral filtering /
- Adaptive sampling

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参考文献(24)

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