TY - JOUR
T1 - U2D2Net
T2 - Unsupervised Unified Image Dehazing and Denoising Network for Single Hazy Image Enhancement
AU - Ding, Bosheng
AU - Zhang, Ruiheng
AU - Xu, Lixin
AU - Liu, Guanyu
AU - Yang, Shuo
AU - Liu, Yumeng
AU - Zhang, Qi
N1 - Publisher Copyright:
© 1999-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - Hazy images captured under ill-posed scenarios with scattering medium (i.e. haze, fog, or smoke) are contaminated in visibility. Inevitably, these images are further degraded by noises owing to real-world imaging. Most existing hazy image enhancement methods perform image dehazing and denoising stage by stage, with the undesirable result that the estimation error of the former stage has to be propagated and amplified in the latter stage, e.g., noise amplification after dehazing. To address this inconsistent degradation, we present an Unsupervised Unified Image Dehazing and Denoising Network, U2D2Net, to remove the haze and suppress the noise simultaneously for a single hazy image. U2D2Net is mainly comprised of an unsupervised dehazing module, an unsupervised denoising module, and a region-similarity fusion strategy. Specifically, we propose an unsupervised transmission-aware dehazing module to restore visibility and suppress depth-dependent noise propagation in the dehazing module. Besides, we design an unsupervised network with a Mean/Max Sub-Sampler in the denoising module. To exploit the correlation and complementary between the previous outputs, a region-similarity fusion strategy is developed to compute the final qualified result. Extensive experiments on both synthetic and real-world datasets illustrate that U2D2Net outperforms other state-of-the-art dehazing and denoising methods in terms of PSNR, SSIM, and subjective visual effects.
AB - Hazy images captured under ill-posed scenarios with scattering medium (i.e. haze, fog, or smoke) are contaminated in visibility. Inevitably, these images are further degraded by noises owing to real-world imaging. Most existing hazy image enhancement methods perform image dehazing and denoising stage by stage, with the undesirable result that the estimation error of the former stage has to be propagated and amplified in the latter stage, e.g., noise amplification after dehazing. To address this inconsistent degradation, we present an Unsupervised Unified Image Dehazing and Denoising Network, U2D2Net, to remove the haze and suppress the noise simultaneously for a single hazy image. U2D2Net is mainly comprised of an unsupervised dehazing module, an unsupervised denoising module, and a region-similarity fusion strategy. Specifically, we propose an unsupervised transmission-aware dehazing module to restore visibility and suppress depth-dependent noise propagation in the dehazing module. Besides, we design an unsupervised network with a Mean/Max Sub-Sampler in the denoising module. To exploit the correlation and complementary between the previous outputs, a region-similarity fusion strategy is developed to compute the final qualified result. Extensive experiments on both synthetic and real-world datasets illustrate that U2D2Net outperforms other state-of-the-art dehazing and denoising methods in terms of PSNR, SSIM, and subjective visual effects.
KW - Haze removal
KW - noise suppression
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85151533421&partnerID=8YFLogxK
U2 - 10.1109/TMM.2023.3263078
DO - 10.1109/TMM.2023.3263078
M3 - Article
AN - SCOPUS:85151533421
SN - 1520-9210
VL - 26
SP - 202
EP - 217
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
ER -