Haze removal for UAV reconnaissance images using layered scattering model

Abstract During the unmanned aerial vehicles (UAV) reconnaissance missions in the middle-low troposphere, the reconnaissance images are blurred and degraded due to the scattering process of aerosol under fog, haze and other weather conditions, which reduce the image contrast and color fidelity. Considering the characteristics of UAV itself, this paper proposes a new algorithm for dehazing UAV reconnaissance images based on layered scattering model. The algorithm starts with the atmosphere scattering model, using the imaging distance, squint angle and other metadata acquired by the UAV. Based on the original model, a layered scattering model for dehazing is proposed. Considering the relationship between wave-length and extinction coefficient, the airlight intensity and extinction coefficient are calculated in the model. Finally, the restored images are obtained. In addition, a classification method based on Bayesian classification is used for classification of haze concentration of the image, avoiding the trouble of manual working. Then we evaluate the haze removal results according to both the subjective and objective criteria. The experimental results show that compared with the origin image, the comprehensive index of the image restored by our method increases by 282.84%, which proves that our method can obtain excellent dehazing effect.

[1]  John P. Oakley,et al.  Improving image quality in poor visibility conditions using a physical model for contrast degradation , 1998, IEEE Trans. Image Process..

[2]  M. C. Hanumantharaju,et al.  Natural Color Image Enhancement Based on Modified Multiscale Retinex Algorithm and Performance Evaluation Using Wavelet Energy , 2013, ISI.

[3]  Dani Lischinski,et al.  Deep photo: model-based photograph enhancement and viewing , 2008, SIGGRAPH 2008.

[4]  Vijayan K. Asari,et al.  Ratio rule and homomorphic filter for enhancement of digital colour image , 2006, Neurocomputing.

[5]  E. J. Mccartney,et al.  Optics of the Atmosphere: Scattering by Molecules and Particles , 1977 .

[6]  Cem Yuksel,et al.  Dual scattering approximation for fast multiple scattering in hair , 2008, SIGGRAPH 2008.

[7]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[8]  D. Mandich.  A comparison between free space optics and 70 GHz short haul links behavior based on propagation model and measured data , 2004, 7th European Conference on Wireless Technology, 2004..

[9]  Raanan Fattal.  Single image dehazing , 2008, SIGGRAPH 2008.

[10]  Huib de Ridder,et al.  Perceptually optimal color reproduction , 1998, Electronic Imaging.

[11]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[12]  Ali M. Reza,et al.  Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement , 2004, J. VLSI Signal Process..

[13]  Wu Hui-zhong.  Method of defogging image of outdoor scenes based on PDE , 2007 .

[14]  Chen Ya-ning.  An improved fog-degraded image clearness algorithm , 2007 .

[15]  Donghua Zhou,et al.  Single image haze removal via depth-based contrast stretching transform , 2014, Science China Information Sciences.

[16]  Zi-Xing Cai,et al.  Objective Assessment Method for the Clearness Effect of Image Defogging Algorithm , 2012 .

[17]  Tu Ya-yuan.  Contrast enhancement algorithm for fog-degraded image based on fuzzy logic , 2008 .

[18]  Shree K. Nayar,et al.  Contrast Restoration of Weather Degraded Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Fabrizio Russo.  An image enhancement technique combining sharpening and noise reduction , 2002, IEEE Trans. Instrum. Meas..

[20]  Zhenyang Wu,et al.  Image enhancement based on the statistics of visual representation , 2005, Image Vis. Comput..

[21]  S. Ansia,et al.  Single image haze removal using white balancing and saliency map , 2015 .

[22]  William J. Fitzgerald,et al.  An Alternative Algorithm for Adaptive Histogram Equalization , 1996, CVGIP Graph. Model. Image Process..

[23]  Shree K. Nayar,et al.  Removing weather effects from monochrome images , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[24]  M. Jourlin,et al.  Logarithmic image processing: The mathematical and physical framework for the representation and processing of transmitted images , 2001 .