Abstract
Physical model-based dehazing methods cannot, in general, avoid environmental variables and undesired artifacts such as non-collected illuminance, halo and saturation since it is difficult to accurately estimate the amount of the illuminance, light transmission and airlight. Furthermore, the haze model estimation process requires very high computational complexity. To solve this problem by directly estimating the radiance of the haze images, we present a novel dehazing and verifying network (DVNet). In the dehazing procedure, we enhanced the clean images by using a correction network (CNet), which uses the ground truth to learn the haze network. Haze images are then restored through a haze network (HNet). Furthermore, a verifying method verifies the error of both CNet and HNet using a self-supervised learning method. Finally, the proposed complementary adversarial learning method can produce results more naturally. Note that the proposed discriminator and generators (HNet & CNet) can be learned via an unpaired dataset. Overall, the proposed DVNet can generate a better dehazed result than state-of-the-art approaches under various hazy conditions. Experimental results show that the DVNet outperforms state-of-the-art dehazing methods in most cases.
Highlights
In outdoor environments, acquired images lose important information such as contrast and salient edges because the particles attenuate the visible light
Many state-of-the-art approaches try to find a better method to estimate the atmospheric light and the transmission map based on reasonable assumptions [10,11,12,13]
For the comparative experiment, we tested existing dehazing methods including: Haze-line prior-based nonlocal dehazing method (NL), densely connected pyramid dehazing net (DCPDN), radiancereflectance optimization based dehazing (RRO), the region-based haze image enhancement method by using triple convolution network(TCN) [15,16,25,30]. Both NL and radiance-reflectance optimization method (RRO) were implemented in Matlab 2016b and tested on i7 CPU equipped with 64 GB of RAM
Summary
In outdoor environments, acquired images lose important information such as contrast and salient edges because the particles attenuate the visible light This degradation is referred to as hazy degradation, which distorts both spatial and color features and decreases visibility of the outdoor object. Many state-of-the-art approaches try to find a better method to estimate the atmospheric light and the transmission map based on reasonable assumptions [10,11,12,13]. He et al proposed a dark channel prior (DCP)-based haze removal method [14]. Shin et al optimized the transmission estimation process using both radiance and reflectance components [16]
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