Automatic image enhancement by learning adaptive patch selection

Na LI; Jian ZHANG

doi:10.1631/FITEE.1700125

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Automatic image enhancement by learning adaptive patch selection

Regular Papers | Updated：2022-05-19

- Automatic image enhancement by learning adaptive patch selection
- 基于学习自适应区域选择的自动增强图像
- Frontiers of Information Technology & Electronic Engineering Vol. 20, Issue 2, Pages: 206-221(2019)
- Affiliations：
  
  School of Science and Technology, Zhejiang International Studies University, Hangzhou 310012, China
- Author bio：
  
  [ "Na LI, E-mail: nli@zisu.edu.cn" ]
  Jian ZHANG, E-mail: jeyzhang@outlook.com
- Funds：
- DOI：10.1631/FITEE.1700125
  CLC： TP391.4
- Received：21 February 2017，
  
  Revised：2019-;1-22，
  
  Published：2019-02
- Accepted：
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Na LI, Jian ZHANG. Automatic image enhancement by learning adaptive patch selection[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 206-221.
DOI：

Na LI, Jian ZHANG. Automatic image enhancement by learning adaptive patch selection[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(2): 206-221. DOI： 10.1631/FITEE.1700125.

摘要

如今数码相机被广泛用于日常摄影。然而，部分照片缺乏细节，需要增强处理。很多现有图像增强算法基于局部区域，而且同一图像所选区域尺寸通常是固定的。用户需手工选择合适的区域尺寸获取最佳图像增强效果。提出一种基于自适应区域选择的自动增强图像算法。该算法采用明暗两个通道，解决各类图像曝光问题。对网上爬取的大量自然图像统计分析获取阈值，自动选择用于通道提取的区域尺寸。该方法可自动增强模糊或者曝光不足/背光的图像，无需任何用户交互。实验结果表明，该算法对现有基于区域的图像增强算法有显著改进。

Abstract

Today

digital cameras are widely used in taking photos. However

some photos lack detail and need enhancement. Many existing image enhancement algorithms are patch based and the patch size is always fixed throughout the image. Users must tune the patch size to obtain the appropriate enhancement. In this study

we propose an automatic image enhancement method based on adaptive patch selection using both dark and bright channels. The double channels enhance images with various exposure problems. The patch size used for channel extraction is selected automatically by thresholding a contrast feature

which is learned systematically from a set of natural images crawled from the web. Our proposed method can automatically enhance foggy or under-exposed/backlit images without any user interaction. Experimental results demonstrate that our method can provide a significant improvement in existing patch-based image enhancement algorithms.

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