Enhancement of low contrast images based on effective space combined with pixel learning

	Enhancement of low contrast images based on effective space combined with pixel learning
	Li, G.; G. Li and G. Han
	2017
发表期刊	Information (Switzerland)
卷号	8 期号:4
摘要	Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model's design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method. 2017 by the authors.
收录类别	sci ; ei
语种	英语
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/58997
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Li, G.,G. Li and G. Han. Enhancement of low contrast images based on effective space combined with pixel learning[J]. Information (Switzerland),2017,8(4).
APA	Li, G.,&G. Li and G. Han.(2017).Enhancement of low contrast images based on effective space combined with pixel learning.Information (Switzerland),8(4).
MLA	Li, G.,et al."Enhancement of low contrast images based on effective space combined with pixel learning".Information (Switzerland) 8.4(2017).

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Enhancement of low c（12469KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载