DCAF-Net: Density-Conditioned Attention Fusion Network for Single-Image Dehazing. [PDF]
Sensors (Basel)Li N +5 more
europepmc +1 more source
ADPCNet: Adaptive Deformable Peripheral Convolution for Efficient Image Dehazing. [PDF]
J ImagingWang Z, Zhu Y, Zheng X, Yang S, Hu C.
europepmc +1 more source
DMSH-Net: Depth-aware multi-scale hybrid vision network for image dehazing. [PDF]
PLoS OneZhao C, Li J, Wang Y, Guo Z, Li X.
europepmc +1 more source
Prompt-based test-time real image dehazing: a novel pipeline [PDF]
Existing methods attempt to improve models\u27 generalization ability on real-world hazy images by exploring well-designed training schemes (\eg, CycleGAN, prior loss).
Lu, Ziqian +4 more
core
Self-Supervised Decoupled Polarization Image Dehazing with an Angle-of-Polarization Frequency-Domain Prior. [PDF]
J ImagingCui L, Zong Y, Kong F, Li J.
europepmc +1 more source
Reference-guided texture transfer with deformable convolutions for indoor image dehazing. [PDF]
Sci RepReyes-Saldaña E, Rivera M.
europepmc +1 more source
, 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
FF-DEIM: DEIM with Image Dehazing and Self-Supervised Pretraining for Catenary Support Component Detection. [PDF]
Sensors (Basel)Zhang L +6 more
europepmc +1 more source
Physics-Inspired Frequency-Decoupled Network for Remote Sensing Image Dehazing. [PDF]
Sensors (Basel)Yang H, Chen X, Xu G.
europepmc +1 more source
A Dual-Channel and Multi-Sensor Fusion Framework for Coal Mine Image Dehazing. [PDF]
Sensors (Basel)Wang X, Huo Y.
europepmc +1 more source