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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.<\/jats:p>","DOI":"10.1007\/s00138-026-01788-y","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T02:35:36Z","timestamp":1769740536000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diffusion-leveraged GAN dehazing driven by classification: a two-stage framework for real-world monitoring imagery"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8367-1212","authenticated-orcid":false,"given":"Moheb M. 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