Diffusion-leveraged GAN dehazing driven by classification: a two-stage framework for real-world monitoring imagery

Abstract This study introduces a novel end-to-end framework for a reliable dehazing of images obtained from an operational flow monitoring network operated by the U.S. Geological Survey (USGS), comprising more than 800 nationwide cameras. A two-stage process that consists of a classifier and a dehazing models is proposed. The classifier acts as a filtering step to detect hazy images and avoid unnecessary operations on haze-free images. The classifier is composed of a frozen ResNet50 feature extractor followed by a custom three-layer fully connected head. The hazy images are then restored using a novel one-step image-to-image translation generative adversarial network model (CycleGAN-Turbo), which consists of two generators and two discriminators. The model leverages diffusion-based architecture. CycleGAN-Turbo was trained on unpaired sets of real hazy and haze-free images. Results show the efficiency of the proposed framework in identifying and restoring hazy images when applied to a network of cameras monitoring river flow conditions. The classifier achieved an overall accuracy of 99.28% with 100% recall in the hazy class. The dehazing model scored 153.29 in FID, 0.73 in CMMD, and 0.0142 in DINO in restoring real-hazy images. A comparison with other state-of-the-art dehazing models shows the superiority of the proposed framework in dehazing real-world hazy images.