Gated Dehazing Network via Least Square Adversarial Learningopen access
- Authors
- Ha, Eunjae; Shin, Joongchol; Paik, Joonki
- Issue Date
- Nov-2020
- Publisher
- MDPI
- Keywords
- haze removal; generative adversarial network; gated structure
- Citation
- SENSORS, v.20, no.21, pp 1 - 15
- Pages
- 15
- Journal Title
- SENSORS
- Volume
- 20
- Number
- 21
- Start Page
- 1
- End Page
- 15
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/43765
- DOI
- 10.3390/s20216311
- ISSN
- 1424-8220
1424-3210
- Abstract
- In a hazy environment, visibility is reduced and objects are difficult to identify. For this reason, many dehazing techniques have been proposed to remove the haze. Especially, in the case of the atmospheric scattering model estimation-based method, there is a problem of distortion when inaccurate models are estimated. We present a novel residual-based dehazing network model to overcome the performance limitation in an atmospheric scattering model-based method. More specifically, the proposed model adopted the gate fusion network that generates the dehazed results using a residual operator. To further reduce the divergence between the clean and dehazed images, the proposed discriminator distinguishes dehazed results and clean images, and then reduces the statistical difference via adversarial learning. To verify each element of the proposed model, we hierarchically performed the haze removal process in an ablation study. Experimental results show that the proposed method outperformed state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), international commission on illumination cie delta e 2000 (CIEDE2000), and mean squared error (MSE). It also gives subjectively high-quality images without color distortion or undesired artifacts for both synthetic and real-world hazy images.
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- Appears in
Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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