Image Dehazing by Joint Estimation of Transmittance and Airlight Using Bi-Directional Consistency Loss Minimized FCN

Very few of the existing image dehazing methods have laid stress on the accurate restoration of color from hazy images, although it is crucial for proper removal of haze. In this paper, we are proposing a Fully Convolutional Neural Network (FCN) based image dehazing method. We have designed a network that jointly estimates scene transmittance and airlight. The network is trained using a custom designed loss, called bi-directional consistency loss, that tries to minimize the error to reconstruct the hazy image from clear image and the clear image from hazy image. Apart from that, to minimize the dependence of the network on the scale of the training data, we have proposed to do both the training and inference in multiple levels. Quantitative and qualitative evaluations show, that the method works comparably with state-of-the-art image dehazing methods.

[1]  Radu Timofte,et al.  O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[2]  Wencheng Wu,et al.  The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations , 2005 .

[3]  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.

[4]  Ketan Tang,et al.  Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Jean-Philippe Tarel,et al.  Vision Enhancement in Homogeneous and Heterogeneous Fog , 2012, IEEE Intelligent Transportation Systems Magazine.

[6]  Ling Shao,et al.  A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior , 2015, IEEE Transactions on Image Processing.

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

[8]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

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

[10]  Gaofeng Meng,et al.  Efficient Image Dehazing with Boundary Constraint and Contextual Regularization , 2013, 2013 IEEE International Conference on Computer Vision.

[11]  Dacheng Tao,et al.  DehazeNet: An End-to-End System for Single Image Haze Removal , 2016, IEEE Transactions on Image Processing.

[12]  Andrea Cavallaro,et al.  Hierarchical rank-based veiling light estimation for underwater dehazing , 2015, BMVC.

[13]  Shai Avidan,et al.  Non-local Image Dehazing , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Radu Timofte,et al.  I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images , 2018, ACIVS.

[15]  Christophe De Vleeschouwer,et al.  D-HAZY: A dataset to evaluate quantitatively dehazing algorithms , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[16]  Raanan Fattal,et al.  Dehazing Using Color-Lines , 2014, ACM Trans. Graph..

[17]  Jizheng Xu,et al.  AOD-Net: All-in-One Dehazing Network , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[18]  Rob Fergus,et al.  Depth Map Prediction from a Single Image using a Multi-Scale Deep Network , 2014, NIPS.

[19]  Cosmin Ancuti,et al.  A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image , 2010, ACCV.

[20]  Xiaochun Cao,et al.  Single Image Dehazing via Multi-scale Convolutional Neural Networks , 2016, ECCV.

[21]  Radu Timofte,et al.  NTIRE 2018 Challenge on Image Dehazing: Methods and Results , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[22]  Bhabatosh Chanda,et al.  Day/night unconstrained image dehazing , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[23]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[24]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[25]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[26]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[27]  Ko Nishino,et al.  Factorizing Scene Albedo and Depth from a Single Foggy Image , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[29]  Christophe De Vleeschouwer,et al.  Night-time dehazing by fusion , 2016, 2016 IEEE International Conference on Image Processing (ICIP).