关键词: 引导滤波/
图像增强/
边缘保持/
正则化
English Abstract
Image enhancement based on multi-guided filtering
Liu Jie1,Zhang Jian-Xun1,2,
Dai Yu1,2
1. College of Computer and Control Engineering, Nankai University, Tianjin 300071, China;
2. Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300071, China
Fund Project:Project supported by the National Key Research and Development Program of China (Grant No. 2017YFC0110402) and the Natural Science Foundation of Tianjin, China (Grant No. 18JCYBJC18800).Received Date:25 July 2018
Accepted Date:11 October 2018
Published Online:05 December 2018
Abstract: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.
Keywords: guided filtering/
image enhancement/
edge preserving/
regularization
