DFR-Net: Density Feature Refinement Network for Image Dehazing Utilizing
  Haze Density Difference
Zhongze Wang Haitao Zhao Lujian Yao Jingchao Peng Kaijie Zhao
In image dehazing task, haze density is a key feature and affects the
performance of dehazing methods. However, some of the existing methods lack a
comparative image to measure densities, and others create intermediate results
but lack the exploitation of their density differences, which can facilitate
perception of density. To address these deficiencies, we propose a
density-aware dehazing method named Density Feature Refinement Network
(DFR-Net) that extracts haze density features from density differences and
leverages density differences to refine density features. In DFR-Net, we first
generate a proposal image that has lower overall density than the hazy input,
bringing in global density differences. Additionally, the dehazing residual of
the proposal image reflects the level of dehazing performance and provides
local density differences that indicate localized hard dehazing or high density
areas. Subsequently, we introduce a Global Branch (GB) and a Local Branch (LB)
to achieve density-awareness. In GB, we use Siamese networks for feature
extraction of hazy inputs and proposal images, and we propose a Global Density
Feature Refinement (GDFR) module that can refine features by pushing features
with different global densities further away. In LB, we explore local density
features from the dehazing residuals between hazy inputs and proposal images
and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to
update local features and pull them closer to clear image features. Sufficient
experiments demonstrate that the proposed method achieves results beyond the
state-of-the-art methods on various datasets.