  Image dehazing, addressing atmospheric interference like fog and haze,
remains a pervasive challenge crucial for robust vision applications such as
surveillance and remote sensing under adverse visibility. While various
methodologies have evolved from early works predicting transmission matrix and
atmospheric light features to deep learning and dehazing networks, they
innately prioritize dehazing quality metrics, neglecting the need for real-time
applicability in time-sensitive domains like autonomous driving. This work
introduces FALCON (Frequency Adjoint Link with CONtinuous density mask), a
single-image dehazing system achieving state-of-the-art performance on both
quality and speed. Particularly, we develop a novel bottleneck module, namely,
Frequency Adjoint Link, operating in the frequency space to globally expand the
receptive field with minimal growth in network size. Further, we leverage the
underlying haze distribution based on the atmospheric scattering model via a
Continuous Density Mask (CDM) which serves as a continuous-valued mask input
prior and a differentiable auxiliary loss. Comprehensive experiments involving
multiple state-of-the-art methods and ablation analysis demonstrate FALCON's
exceptional performance in both dehazing quality and speed (i.e., >$180
frames-per-second), quantified by metrics such as FPS, PSNR, and SSIM.
