Article
Photo-Realistic Image Dehazing and Verifying Networks via
Complementary Adversarial Learning
Joongchol Shin and Joonki Paik *
Citation: Shin, J.; Paik, J.
Photo-Realistic Image Dehazing and
Verifying Networks via
Complementary Adversarial
Learning. Sensors 2021, 21, 6182.
https://doi.org/10.3390/s21186182
Academic Editors: YangQuan Chen,
Subhas Mukhopadhyay, Nunzio
Cennamo, M. Jamal Deen, Junseop
Lee and Simone Morais
Received: 9 August 2021
Accepted: 13 September 2021
Published: 15 September 2021
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4.0/).
Department of Image, Chung-Ang University, Seoul 06974, Korea; jcshin@ipis.cau.ac.kr
* Correspondence: paikj@cau.ac.kr
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.
Keywords: dehazing; GAN; CNN
1. Introduction
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. If the hazy degradation is not restored, we cannot expect
a good performance of main image processing or image analysis methods such as object
detection, image matching, and imaging systems [
1
–
4
], to name a few. Therefore, the
common goal of dehazing algorithms is to enhance the edge and contrast while suppressing
intensity or color saturation. To the best of the authors’ knowledge, Middleton and Edgar
were the first to employ a physical haze model for the dehazing problem [5].
To generate the haze-free image using the physical model, atmospheric light and the
corresponding transmission should be estimated. However, an accurate estimation of the
atmospheric light and transmission map generally requires additional information, such as
a pair of polarized images, multiple images under different weather conditions, distance
maps, or user interactions [
6
–
9
]. For that reason, 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
–
13
]. He et al. proposed a dark channel prior (DCP)-based haze
removal method [
14
]. They assumed that pixels in the local patch of a clear image have
at least one dark pixel. The DCP method works well in most regions that satisfy the DCP
assumption, but fails in a white object region. Berman et al. estimated the transmission
map using haze-line prior assumption that the pixel coordinates in the color space tend to
become closer to the atmospheric light in a hazy image [
15
]. To find the lower bound of a
haze-line, they used the 500 representative colors. While the Berman’s approach enhances
Sensors 2021, 21, 6182. https://doi.org/10.3390/s21186182 https://www.mdpi.com/journal/sensors
