低通滤波下约束对数强度熵的图像渐晕校正

doi:10.11772/j.issn.1001-9081.2019101809

计算机应用 ›› 2020, Vol. 40 ›› Issue (6): 1812-1817.DOI: 10.11772/j.issn.1001-9081.2019101809

• 虚拟现实与多媒体计算 • 上一篇下一篇

低通滤波下约束对数强度熵的图像渐晕校正

周思羽¹, 包国琦², 刘凯¹

1.四川大学电气工程学院，成都 610065
2.广东省公安厅，广州 510050

收稿日期:2019-10-25 修回日期:2019-12-16 出版日期:2020-06-10 发布日期:2020-06-18
通讯作者: 刘凯(1973—)
作者简介:周思羽(1995—)，女，重庆人，硕士研究生，主要研究方向：图像处理、实时三维成像.包国琦(1974—)，男，河南舞阳人，高级工程师，硕士，主要研究方向：信息安全.刘凯(1973—)，男，江苏无锡人，教授，博士，主要研究方向：结构光三维成像、数字图像处理.
基金资助:
国家自然科学基金资助项目(61473198)；四川省科技厅重点研发项目（2018GZ0198）；成都市科技局技术创新研发项目（2018-YFYF-00029-GX）；四川大学自贡市校地科技合作重点研发项目（2018CDZG-12）。

lmage vignetting correction based on constrained log-intensity entropy under low-pass filtering

ZHOU Siyu¹, BAO Guoqi², LIU Kai¹

1. College of Electrical Engineering, Sichuan University, Chengdu Sichuan 610065, China
2. Guangdong Public Security Department, Guangzhou Guangdong 510050, China

Received:2019-10-25 Revised:2019-12-16 Online:2020-06-10 Published:2020-06-18
Contact: LIU Kai, born in 1973, Ph. D., professor. His research interests include structured light three-dimensional imaging, digital image processing.
About author:ZHOU Siyu, born in 1995, M. S. candidate. Her research interests include image processing, real-time three-dimensional imaging.BAO Guoqi, born in 1974, M. S., senior engineer. His research interests include information security.LIU Kai, born in 1973, Ph. D., professor. His research interests include structured light three-dimensional imaging, digital image processing.
Supported by:
National Natural Science Foundation of China (61473198), the Key Research and Development Program of Sichuan Science and Technology Department (2018GZ0198), the Technology Innovation Research and Development Program of Chengdu Science and Technology Bureau (2018-YFYF-00029-GX), the Key Research and Development Program of School-Land Science and Technology Cooperation in Zigong City, Sichuan University (2018CDZG-12).

摘要/Abstract

摘要： 渐晕是图像中像素光强沿径向方向衰减的现象，为了解决其对计算机视觉任务和图像处理精度的影响，提出了低通滤波下约束对数强度熵的图像渐晕校正方法。首先，使用偶数项的六阶多项式函数建立渐晕模型；其次，通过低通滤波计算最小的目标图像对数强度熵，在目标值的约束下求出既满足渐晕函数变化规律又能减小图像的对数强度熵的渐晕模型的最优参数解；最后，对渐晕图像采用渐晕模型的逆向补偿校正渐晕。采用结构相似性指标（SSIM）和均方根误差(RMSE)度量渐晕校正效果，结果表明，所提方法不仅能有效恢复渐晕区域的亮度信息，得到真实、自然的无渐晕图像，而且能有效校正不同程度的渐晕，渐晕校正结果视觉一致性良好。

关键词: 图像处理, 径向衰减, 渐晕校正, 低通滤波, 对数强度熵

Abstract: Vignetting is the phenomenon that the intensity of the pixel in the image decreases along the radial direction. In order to solve the problem that it affects the accuracy of computer vision task and image processing, a method of single image vignetting correction based on constrained log-intensity entropy under low-pass filtering was proposed. Firstly, the vignetting model was established by using a sixth order polynomial function of even term. Secondly, the minimum log-intensity entropy of the target image was calculated by low-pass filtering. Under the constraint of the target value, the optimal parameter solution of the vignetting model was obtained, which can satisfy the change rule of the vignetting function and reduce the log-intensity entropy of the image. Finally, vignetting was eliminated by using inverse compensation of vignetting model. Vignetting correction results were evaluated by Structural SIMilarity index (SSIM) and Root Mean Square Error (RMSE). Experimental results show that the proposed method can not only effectively recover the brightness information of the vignetting area to get real and natural non-vignetting image, but also effectively correct the different degrees of vignetting with a good visual consistency.

Key words: image processing, radial attenuation, vignetting correction, low-pass filtering, log-intensity entropy

中图分类号:

TP751.1

周思羽, 包国琦, 刘凯. 低通滤波下约束对数强度熵的图像渐晕校正[J]. 计算机应用, 2020, 40(6): 1812-1817.

ZHOU Siyu, BAO Guoqi, LIU Kai. lmage vignetting correction based on constrained log-intensity entropy under low-pass filtering[J]. Journal of Computer Applications, 2020, 40(6): 1812-1817.

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低通滤波下约束对数强度熵的图像渐晕校正

lmage vignetting correction based on constrained log-intensity entropy under low-pass filtering

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