Single image defogging based on particle swarm optimization

Due to the lack of enough information to solve the equation of image degradation model, existing defogging methods generally introduce some parameters and set these values fixed. Inappropriate parameter setting leads to difficulty in obtaining the best defogging results for different input foggy images. Therefore, a single image defogging algorithm based on particle swarm optimization (PSO) is proposed in this letter to adaptively and automatically select optimal parameter values for image defogging algorithms. The proposed method is applied to two representative defogging algorithms by selecting the two main parameters and optimizing them using the PSO algorithm. Comparative study and qualitative evaluation demonstrate that the better quality results are obtained by using the proposed parameter selection method.

[1]  G. Wiselin Jiji,et al.  Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique , 2015, Appl. Soft Comput..

[2]  Jean-Philippe Tarel,et al.  Enhanced fog detection and free-space segmentation for car navigation , 2014, Machine Vision and Applications.

[3]  Qiang Chen,et al.  A moment-based nonlocal-means algorithm for image denoising , 2009, Inf. Process. Lett..

[4]  Xianghui Shen,et al.  Image Haze Removal Using Dark Channel Prior , 2013 .

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Andrea Lagorio,et al.  Automatic Detection of Adverse Weather Conditions in Traffic Scenes , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[7]  Ko Nishino,et al.  Bayesian Defogging , 2012, International Journal of Computer Vision.

[8]  Yangyang Li,et al.  Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation , 2015, Inf. Sci..

[9]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[10]  Jean-Philippe Tarel,et al.  Towards Fog-Free In-Vehicle Vision Systems through Contrast Restoration , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Jian Sun,et al.  Single image haze removal using dark channel prior , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Robby T. Tan,et al.  Visibility in bad weather from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Zixing Cai,et al.  Objective measurement for image defogging algorithms , 2014 .

[14]  Jean-Philippe Tarel,et al.  Fast visibility restoration from a single color or gray level image , 2009, 2009 IEEE 12th International Conference on Computer Vision.