Abstract:
Haze during bad weather degrades visibility of the scene drastically. Degradation of the scene visibility varies concerning the
transmission map (TrMap) of the scene. Estimation of accurate TrMap is a key step to reconstruct the haze-free scene. Previous
approaches, DehazeNet (Cai et al. in IEEE Trans Image Process 25(11):5187–5198, 2016), MSCNN (Ren et al., in: European
conference on computer vision, Springer, New York, 2016), overcome the drawbacks of handcrafted features. However, they
fail to recover the haze-free scene without color distortion. In this paper, cardinal color (red, green and blue) fusion network
is proposed to estimate the robust TrMap. Proposed network recovers haze-free scene without color distortion. In the frst
stage, it generates multi-channel depth maps using a novel parallel convolution flter bank. The second stage comprises of
multi-scale convolution flter bank which extracts the scale-invariant features. Further, spatial pooling followed by convolution
flter estimates the robust scene transmission map. In the last stage, estimated TrMap is given to the optical model to recover
the haze-free scene. Three benchmark datasets namely D-HAZY (Ancuti et al., in: 2016 IEEE international conference on
image processing (ICIP), 2016), ImageNet (Deng et al., in: IEEE conference on computer vision and pattern recognition,
2009 (CVPR 2009), 2009) and a set of real-world hazy scenes are used to analyze the efectiveness of the proposed approach.
Structural similarity index, mean square error and peak signal-to-noise ratio are used to evaluate the proposed network for
single image haze removal. Further, the efect of haze on object detection is analyzed and robust cascaded pipeline, i.e., haze
removal followed by Faster RCNN is proposed for object detection in the hazy environment. Performance analysis shows that
the proposed network estimates robust TrMap and recovers haze-free scene without color distortion.
