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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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[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
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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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