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图像增强技术可以有效地突出图像中的有用信息，已广泛应用于多个领域.现有的图像增强算法往往无法应对自然图像中复杂的梯度分布，难以准确保持图像中前景与背景的边缘信息.为了改善输出图像的边界过平滑问题，本文提出了一个基于多引导滤波的图像增强算法.首先，设计了一个以滤波核为变量的通用图像优化模型，现有的联合滤波器可视为该模型的解；然后，依据集成学习的思想，将联合滤波器中的单幅引导图像扩展到多幅，以更好地利用引导图中的结构信息进而获得更好的输出结果，并给出了一个多幅引导图的来源途径；最后，对多幅输出图像进行平滑，在图像优化模型中加入正则化项，以确保由多引导滤波得到的不同滤波输出保持一致.实验结果表明，本文算法在抑制图像噪声的同时，可以更好地保留物体的边界信息，从而使图像的信噪比进一步提升.Image enhancement, as a basic image proicessing technique, contains much research content, such as enhance contrast, image restoration, noise reduction, image sharpening, distortion correction, etc. The purpose of image enhancement is to effectively highlight the useful information in target image and suppress noise as well. The conventional image enhancement methods are always powerless to tackle the complicated gradient distributions in natural images, and they are also difficult to retain the information about edges accurately. For improving the status of over-smoothing on boundaries, we propose an image enhancement method based on multi-guided filtering. We first synthetically analyze the property of joint filtering and propose the general image optimization model in which the variable parameter is filter kernel. Different filter kernel in the optimization model above generate different filtering method. That is to say, we can use this model to describe the image enhancement problems. The existing joint filters can be regarded as close form solutions of the optimization model above. Inspired by ensemble theory, we use multiple guided images in joint filtering instead of a single guided image to make full use of structure information. By doing so, the image enhancement based on multi-guided filtering can obtain more accurate filtering results. In order to keep the consistency among the multiple filtering outputs of multi-guided filtering method, we add a regularization term into a general image optimization model. We also take into consideration the consistency of pixels in the same image. The experimental results about the noise reduction and image enhancement show that the image enhancement based on multi-guided filtering can give rise to significant outputs. The peak-signal-to-noise ratio of output image of proposed method is higher than those from the traditional image enhancement methods. Therefore, the image enhancement based on multi-guided filtering can improve the quality of digital images efficiently and effectively. This provides a good precondition for subsequent image processing steps and has a prospect of very wide application.
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Keywords:
- guided filtering /
- image enhancement /
- edge preserving /
- regularization
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[16] Dai L Q, Yuan M K, Li Z C, Zhang X P, Tang J H 2017 IEEE Conference on Computer Vision and Pattern Recognition Hawaii, USA, July 21-26, 2017 p4905
[17] Wu H K, Zheng S, Zhang J, Huang K Q 2018 IEEE Conference on Computer Vision and Pattern Recognition Salt Lake City, Utah, June 18-22, 2018
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[21] Xu L, Yan Q, Xia Y, Jia J Y 2012 ACM Trans. Graph. 31 139
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[1] Rahman Z U, Jobson D J 2004 J. Electron. Imaging 13 100
[2] Seow M J, Asari V K 2006 Neurocomputing 69 954
[3] Kimmel R, Elad M, Shaked D, Keshet R, Sobel L 2003 Int. J. Comput. Vision 52 7
[4] Rong Z, Li Z, Li D N 2015 Optik 126 5665
[5] Zeng L, Chen J, Tong L, Yan B, Ping X J 2013 Proceedings of the International Conference on Medical Imaging Physics and Engineering Shenyang, China, October 19-20, 2013 p269
[6] Perona P, Malik J 1990 IEEE Trans. Pattern Anal. Mach. Intell. 12 629
[7] Petschnigg G, Agrawala M, Hoppe H, Szeliski R, Cohen M, Toyama K 2004 ACM Trans. Graph. 23 664
[8] Tomasi C, Manduchi R 1998 IEEE International Conference on Computer Vision Bombay, India January 4-7, 1998 p839
[9] Aurich V, Weule J 1995 Proceedings of DAGM Symposium London, UK, September 13-15, 1995 p538
[10] He K M, Sun J, Tang X O 2013 IEEE Trans. Pattern Anal. Mach. Intell. 35 1397
[11] Gastal E S L, Oliveira M M 2011 ACM Trans. Graph. 30 69
[12] Kou F, Chen W H, Wen C Y, Li Z G 2015 IEEE Trans. Image Process. 24 4528
[13] Li Z G, Zheng J H, Zhu Z J, Yao W, Wu S Q 2014 IEEE Trans. Image Process. 24 120
[14] Zhang Q, Shen X Y, Xu L, Jia J Y 2014 European Conference on Computer Vision Zurich, Switzerland, September 6-12, 2014 p815
[15] Dai L Q, Yuan M K, Zhang F H, Zhang X P 2015 IEEE International Conference on Computer Vision Santiago, Chile, December 11-18, 2015 p352
[16] Dai L Q, Yuan M K, Li Z C, Zhang X P, Tang J H 2017 IEEE Conference on Computer Vision and Pattern Recognition Hawaii, USA, July 21-26, 2017 p4905
[17] Wu H K, Zheng S, Zhang J, Huang K Q 2018 IEEE Conference on Computer Vision and Pattern Recognition Salt Lake City, Utah, June 18-22, 2018
[18] Ham B, Cho M, Ponce J 2018 IEEE Trans. Pattern Anal. Mach. Intell. 40 192
[19] Thai B, Alnasrawi M, Deng G, Su Z 2017 IET Image Proc. 11 512
[20] Farbman Z, Fattal R, Lischinski D, Szeliski R 2008 ACM Trans. Graph. 27 67
[21] Xu L, Yan Q, Xia Y, Jia J Y 2012 ACM Trans. Graph. 31 139
[22] Milanfar P 2013 IEEE Signal Process. Mag. 30 106
[23] Xu L, Lu C, Xu Y, Jia J Y 2011 ACM Trans. Graph. 30 174
[24] Porikli F 2008 IEEE Conference on Computer Vision and Pattern Recognition Anchorage, Alaska, USA, June 23-28, 2008 p3895
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