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The main advantages of the method are high computational speed and using unpaired image data for training. The method adapts the Zero-DCE approach (Li et al. in IEEE Trans Pattern Anal Mach Intell 44(8):4225\u20134238, 2021) for the image dehazing problem and uses high-order curves to adjust the dynamic range of images and achieve dehazing. Training the proposed dehazing neural network does not require paired hazy and clear datasets but instead utilizes a set of loss functions, assessing the quality of dehazed images to drive the training process. Experiments on a large number of real-world hazy images demonstrate that our proposed network effectively removes haze while preserving details and enhancing brightness. Furthermore, on an affordable GPU-equipped laptop, the processing speed can reach 1000 FPS for images with 2K resolution, making it highly suitable for real-time dehazing applications.<\/jats:p>","DOI":"10.1007\/s00138-024-01601-8","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T03:02:44Z","timestamp":1724900564000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fast no-reference deep image dehazing"],"prefix":"10.1007","volume":"35","author":[{"given":"Hongyi","family":"Qin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander G.","family":"Belyaev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,29]]},"reference":[{"issue":"12","key":"1601_CR1","first-page":"2341","volume":"33","author":"K He","year":"2010","unstructured":"He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. 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