FALCON: Fast Image Haze Removal Leveraging Continuous Density Mask
Donghyun Kim, Seil Kang, Seong Jae Hwang · 2025
Image dehazing, eliminating atmospheric interference, remains a pervasive challenge crucial for robust vision applications such as surveillance and remote sensing under adverse visibility. While various methodologies have evolved, they innately prioritize dehazing quality metrics, neglecting the need for real-time applicability in time-sensitive domains like autonomous driving. Considering the need for a pioneering hybrid paradigm in dehazing, we present FALCON, an image dehazing system achieving state-of-theart performance on both quality and speed. Particularly, we leverage the underlying haze distribution via a novel approach called Continuous Density Mask (CDM). CDM serves as a continuous-valued mask input prior and auxiliary loss, allowing model to explicitly identify pixel-wise haze density. We also implement the haze density calculation in a differentiable manner. Further, we introduce a low model-workload recipe that globally expand the receptive field by adding a single bottleneck module to$U$-Net. The experimental results demonstrate FALCON's exceptional performance in both dehazing quality and speed.