Different from general algorithms of haze removal and low lighting image enhancement, which only use the information of image to process, this paper adds a reference image to get more information for the algorithm and focuses on enhancing region of interest of an image based on the reference one. With the reference image, the haze one can be divided into Region of Interest (RoI) and Region of no Interest (non-RoI). Furthermore, the reference image can provide more useful information for computing the transmission map and atmospheric light. For the non-RoI region, a more robust transmission map and minimizing reconstruction error cost function based method to estimate atmospheric light has been proposed. Because the atmospheric light is a global variable, the optimized one is also suitable for the RoI region. With the global optimized atmospheric light, an optimized transmission map can be got for the RoI region. The RoI region can be enhanced via the optimal transmission map and atmosphere light. Theoretical analysis gives eloquent proof proving that the proposed method is definitely better than the traditional dark-channel-prior-based methods due to our better transmission map and atmosphere light. Extensive experiments also show the expected results.
[1]
Raanan Fattal,et al.
Single image dehazing
,
2008,
ACM Trans. Graph..
[2]
Jiangtao Wen,et al.
Fast efficient algorithm for enhancement of low lighting video
,
2010,
2011 IEEE International Conference on Multimedia and Expo.
[3]
Mohinder Malhotra.
Single Image Haze Removal Using Dark Channel Prior
,
2016
.
[4]
Shree K. Nayar,et al.
Chromatic framework for vision in bad weather
,
2000,
Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[5]
Shree K. Nayar,et al.
Vision and the Atmosphere
,
2002,
International Journal of Computer Vision.
[6]
Robby T. Tan,et al.
Visibility in bad weather from a single image
,
2008,
2008 IEEE Conference on Computer Vision and Pattern Recognition.
[7]
Sabine Süsstrunk,et al.
Color image dehazing using the near-infrared
,
2009,
2009 16th IEEE International Conference on Image Processing (ICIP).