TY - GEN
T1 - Dense '123' color enhancement dehazing network
AU - Guo, Tiantong
AU - Cherukuri, Venkateswararao
AU - Monga, Vishal
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Single image dehazing has gained much attention recently. A typical learning based approach uses example hazy and clean image pairs to train a mapping between the two. Of the learning based methods, those based on deep neural networks have shown to deliver state of the art performance. An important aspect of recovered image quality is the color information, which is severely compromised when the image is corrupted by very dense haze. While many different network architectures have been developed for recovering dehazed images, an explicit attention to recovering individual color channels with a design that ensures their quality has been missing. Our proposed work, focuses on this issue by developing a novel network structure that comprises of: a common DenseNet based feature encoder whose output branches into three distinct DensetNet based decoders to yield estimates of the R, G and B color channels of the image. A subsequent refinement block further enhances the final synthesized RGB/color image by joint processing of these color channels. Inspired by its structure, we call our approach the One-To-Three Color Enhancement Dehazing (123-CEDH) network. To ensure the recovery of physically meaningful and high quality color channels, the main network loss function is further regularized by a multi-scale structural similarity index term as well as a term that enhances color contrast. Experiments reveal that 123-CEDH has the ability to recover color information at early training stages (i.e. in the first few epochs) vs. other highly competitive methods. Validation on the benchmark datasets of the NTIRE'19 and NTIRE'18 dehazing challenges reveals the 123-CEDH to be one of the Top-3 methods based on results released in the NTIRE'19 competition.
AB - Single image dehazing has gained much attention recently. A typical learning based approach uses example hazy and clean image pairs to train a mapping between the two. Of the learning based methods, those based on deep neural networks have shown to deliver state of the art performance. An important aspect of recovered image quality is the color information, which is severely compromised when the image is corrupted by very dense haze. While many different network architectures have been developed for recovering dehazed images, an explicit attention to recovering individual color channels with a design that ensures their quality has been missing. Our proposed work, focuses on this issue by developing a novel network structure that comprises of: a common DenseNet based feature encoder whose output branches into three distinct DensetNet based decoders to yield estimates of the R, G and B color channels of the image. A subsequent refinement block further enhances the final synthesized RGB/color image by joint processing of these color channels. Inspired by its structure, we call our approach the One-To-Three Color Enhancement Dehazing (123-CEDH) network. To ensure the recovery of physically meaningful and high quality color channels, the main network loss function is further regularized by a multi-scale structural similarity index term as well as a term that enhances color contrast. Experiments reveal that 123-CEDH has the ability to recover color information at early training stages (i.e. in the first few epochs) vs. other highly competitive methods. Validation on the benchmark datasets of the NTIRE'19 and NTIRE'18 dehazing challenges reveals the 123-CEDH to be one of the Top-3 methods based on results released in the NTIRE'19 competition.
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U2 - 10.1109/CVPRW.2019.00266
DO - 10.1109/CVPRW.2019.00266
M3 - Conference contribution
AN - SCOPUS:85081970166
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 2131
EP - 2139
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PB - IEEE Computer Society
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Y2 - 16 June 2019 through 20 June 2019
ER -