光学合成孔径复原图像的振铃探测与消除

魏小峰; 耿则勋; 宋向

doi:10.3788/OPE.20142211.3091

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光学合成孔径复原图像的振铃探测与消除

信息科学 | 更新时间：2020-08-13

- 光学合成孔径复原图像的振铃探测与消除
- Detection and removal of ringing artifact for optical synthetic aperture restoration image
- 光学精密工程 2014年22卷第11期页码：3091-3099
- 作者机构：
  
  1. 解放军信息工程大学地理空间信息学院,河南郑州,450002
  2. 61175部队,江苏南京,210049
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2015AA123703)
- DOI：10.3788/OPE.20142211.3091
  中图分类号： TP751
- 收稿日期：2014-04-17，
  
  修回日期：2014-05-26，
  
  纸质出版日期：2014-11-25
- 稿件说明：
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魏小峰, 耿则勋, 宋向. 光学合成孔径复原图像的振铃探测与消除[J]. 光学精密工程, 2014,22(11): 3091-3099

WEI Xiao-feng, GENG Ze-xun, SONG Xiang. Detection and removal of ringing artifact for optical synthetic aperture restoration image[J]. Editorial Office of Optics and Precision Engineering, 2014,22(11): 3091-3099
魏小峰, 耿则勋, 宋向. 光学合成孔径复原图像的振铃探测与消除[J]. 光学精密工程, 2014,22(11): 3091-3099 DOI： 10.3788/OPE.20142211.3091.

WEI Xiao-feng, GENG Ze-xun, SONG Xiang. Detection and removal of ringing artifact for optical synthetic aperture restoration image[J]. Editorial Office of Optics and Precision Engineering, 2014,22(11): 3091-3099 DOI： 10.3788/OPE.20142211.3091.

摘要

针对相位平移误差使光学合成孔径图像的复原出现的振铃效应

提出了一种结合视觉感知的振铃探测与消除方法。首先

提出并利用梯度方向随机度和局部方差值两项指标计算得到图像纹理区;然后

根据不同阈值的Canny算子检测出全边缘与主边缘

并将主边缘邻域内的全边缘作为待筛选的边缘振铃;最后

结合人类视觉系统的纹理掩盖效应分别得到振铃区与平坦区。此外

在图像分区基础上

选择亮度相似度因子作为自适应参数

利用双边滤波进行图像处理。实验显示:处理后Lena图像的峰值信噪比提高了10.8%

分辨率板的结构相似度提高了0.057。结果表明:该方法能够对图像以像素精度分区

可以在保持边缘与纹理的同时

有效消除振铃效应

提高图像复原质量。

Abstract

As phase shift errors will lead to severe ringing artifacts in the restoration results of optical synthetic aperture images

a method to detect and remove the ringing artifacts was proposed based on visual perception. Firstly

the texture region was obtained by calculating the scales of gradient direction randomness and local variance distribution. Then

the whole edges and main edges were separately detected by the Canny operator with different thresholds and whole edges belong to the neighborhood of all edges were considered as the edge ringing candidate. Finally

the ringing region and flat region were respectively derived by combining the texture masking of human visual system. Based on the image classification

the adaptive bilateral filter was applied to removal of the ringing artifacts. Experimental results show that the Peak Signal to Noise Ratio ( PSNR) of a Lena image after processing is improved by 10.8% while the Structural Similarity(SSIM) of resolution board image is improved by 0.057 as well. It condudes that the proposed method classifies the image on pixel scale

so it eliminates the ringing artifacts dramatically while preserving the edges and textures effectively. It is capable of improving the image restoration quality.

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Keywords

references

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