计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 1117-1120.DOI: 10.11772/j.issn.1001-9081.2017092388

• 虚拟现实与多媒体计算 • 上一篇    下一篇

单幅粉尘图像的切片重构恢复方法

王园宇, 张一凡, 王云飞   

  1. 太原理工大学 计算机科学与技术学院, 太原 030024
  • 收稿日期:2017-10-09 修回日期:2017-11-09 出版日期:2018-04-10 发布日期:2018-04-09
  • 通讯作者: 王园宇
  • 作者简介:王园宇(1973-),男,山西汾阳人,讲师,博士,主要研究方向:机器人视觉、图像处理、模式识别;张一凡(1993-),女,山西忻州人,硕士研究生,主要研究方向:图像处理、模式识别;王云飞(1994-),男,山西晋中人,硕士研究生,主要研究方向:虚拟现实。
  • 基金资助:
    山西省自然科学基金资助项目(2012011014-2)。

Slices reconstruction method for single image dedusting

WANG Yuanyu, ZHANG Yifan, WANG Yunfei   

  1. College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan Shanxi 030024, China
  • Received:2017-10-09 Revised:2017-11-09 Online:2018-04-10 Published:2018-04-09
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Shanxi Province (2012011014-2).

摘要: 针对粉尘分布不均匀且局部多散射引起的图像退化问题,提出单幅粉尘图像的切片重构恢复方法。首先,在场景深度方向采用McCartney模型得到多层切片图像;其次,对切片图像进行联合粉尘检测运算,将切片图像中无粉尘的区域保留,而将有粉尘的区域标记并将此区域作为下一张切片图像的检测区域;然后,对每张切片图像中保留的无粉尘部分和最后一张切片的粉尘区域进行重构;最后,对重构图像使用快速导向滤波,实现了对单幅粉尘图像的恢复。实验结果表明,该恢复方法可以对图像中的粉尘区域实现快速、有效去除,为粉尘环境中基于计算机视觉的目标监控与识别工作奠定基础。

关键词: 图像恢复, 粉尘检测, 图像重构, 多散射, 单幅图像

Abstract: In order to solve the image degradation in the non-uniform dust environment with multiple scattering lights, a slices reconstruction method for single image dedusting was proposed. Firstly, the slices along the depth orientation were produced based on McCartney model in dust environment. Secondly, the joint dust detection method was used to detect dust patches in the slices where non-dust areas were reserved but the dust zones were marked as the candidate detected areas of the next slice image. Then, an image was reconstructed by combining these non-dust areas of each slice and the dust zone of the last slice. Finally, a restored image was obtained by a fast guided filter which was applied to the reconstructed area. The experimental results prove that the proposed restoration method can effectively and quickly get rid of dust in the image, and lay the foundation of object detection and recognition work based on computer vision in dust environment.

Key words: image restoration, dust detection, image reconstruction, multiple scattering, single image

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