DMSH-Net: Depth-aware multi-scale hybrid vision network for image dehazing. [PDF]
PLoS OneZhao C, Li J, Wang Y, Guo Z, Li X.
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Fooling the Image Dehazing Models by First Order Gradient
The research on the single image dehazing task has been widely explored. However, as far as we know, no comprehensive study has been conducted on the robustness of the well-trained dehazing models.
Peng, Chengwei +4 more
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Self-Supervised Decoupled Polarization Image Dehazing with an Angle-of-Polarization Frequency-Domain Prior. [PDF]
J ImagingCui L, Zong Y, Kong F, Li J.
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, 2018 The aim of this work is to find a method for removing haze from satellite imagery. This is done by taking two algorithms developed for images taken from the sur- face of the earth and adapting them for satellite images. The two algorithms are Single Image Haze Removal Using Dark Channel Prior by He et al.
openaire +1 more source
Reference-guided texture transfer with deformable convolutions for indoor image dehazing. [PDF]
Sci RepReyes-Saldaña E, Rivera M.
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FF-DEIM: DEIM with Image Dehazing and Self-Supervised Pretraining for Catenary Support Component Detection. [PDF]
Sensors (Basel)Zhang L +6 more
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Physics-Inspired Frequency-Decoupled Network for Remote Sensing Image Dehazing. [PDF]
Sensors (Basel)Yang H, Chen X, Xu G.
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A Dual-Channel and Multi-Sensor Fusion Framework for Coal Mine Image Dehazing. [PDF]
Sensors (Basel)Wang X, Huo Y.
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CL-ODGAN: an unpaired attention-guided GAN framework for remote sensing image dehazing. [PDF]
Sci RepZhang H, Mu X, Yin B.
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CSGL-Former: Cross-Stripes Global-Local Fusion Transformer for Remote Sensing Image Dehazing. [PDF]
Sensors (Basel)Feng S, Zhang X, Yuan J, Zhu Y.
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