Nonhomogeneous Image Dehazing
9 papers with code • 1 benchmarks • 2 datasets
The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light.
Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Conventional patch-based haze removal algorithms (e. g. the Dark Channel prior) usually performs dehazing with a fixed patch size.
In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously.
Learning to dehaze single hazy images, especially using a small training dataset is quite challenging.
This paper proposes an end-to-end Efficient Re-parameterizationResidual Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image.
A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for single image dehazing scheme is proposed in this paper.
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog.
This work aims to improve the applicability of diffusion models in realistic image restoration.