Edge-guided light field image saliency detection

LIANG Xiao; DENG Hui-ping; XIANG Sen; WU Jin

doi:10.37188/CJLCD.2022-0239

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Edge-guided light field image saliency detection

Image Processing | 更新时间：2023-05-09

- Edge-guided light field image saliency detection
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 5, Pages: 644-655(2023)
- 作者机构：
  
  1.武汉科技大学信息科学与工程学院，湖北武汉 430081
  2.武汉科技大学冶金自动化与检测技术教育部工程研究中心，湖北武汉 430081
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61702384;61502357)
- DOI：10.37188/CJLCD.2022-0239
  CLC：
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LIANG Xiao, DENG Hui-ping, XIANG Sen, et al. Edge-guided light field image saliency detection. [J]. Chinese Journal of Liquid Crystals and Displays 38(5):644-655(2023)
DOI：

LIANG Xiao, DENG Hui-ping, XIANG Sen, et al. Edge-guided light field image saliency detection. [J]. Chinese Journal of Liquid Crystals and Displays 38(5):644-655(2023) DOI： 10.37188/CJLCD.2022-0239.

摘要

针对光场图像显著性检测存在检测目标不完整、边缘模糊的问题，本文提出了一种基于边缘引导的光场图像显著性检测方法。利用边缘增强网络提取全聚焦图像的主体图和边缘增强图，结合主体图和焦堆栈图像所提取的特征获得初始显著图，以提高检测结果的准确性和完整性；将初始显著图和边缘增强图通过特征融合模块进一步学习边缘特性的信息，突出边缘细节信息；最后，使用边界混合损失函数优化以获得边界更为清晰的显著图。实验结果表明，本文所提出的网络在最新的光场图像数据集上，F-measure和MAE分别为0.88和0.046，表现均优于现有的RGB图像、RGB-D 图像和光场图像显著性检测算法。所提方法能够更加精确地从复杂场景中检测出完整的显著对象，获得边缘清晰的显著图。

Abstract

Aiming at the problems of incomplete detection targets and blurred edges in light field image saliency detection， this paper proposes an edge-guided light field image saliency detection method. The edge enhancement network is used to extract the main image and edge enhancement image of all-focus image， and the initial saliency map is obtained by combining the features extracted from the main image and focal stack image to improve the accuracy and completeness of detection results. The initial saliency map and edge enhancement further learns the information of edge characteristics and highlights the edge details through the feature fusion module. Finally， the boundary mixing loss function is used to optimize the saliency map with clearer boundaries. The experimental results show that on the latest light field data set， F-measure and MAE are 0.88 and 0.046 respectively， which are better than the existing RGB images， RGB-D images and light field image saliency detection algorithms. The proposed method can more accurately detect complete salient objects from complex scenes， and obtain saliency maps with clear edges.

关键词

显著性检测深度学习光场图像卷积神经网络边缘检测网络

Keywords

saliency detectiondeep learninglight field imageconvolutional neural networkedge detection network

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