GACR reformulates cloud removal as an observation-anchored residual inversion process with geo-contextual prior alignment to preserve semantic structures for improved downstream interpretation tasks.
IEEE transactions on pattern analysis and machine intelligence33(12), 2341–2353 (2010)
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RTE-FM-Dehazer trains a flow-matching model with an RTE-derived diffusion-absorption regularizer on a new 50k real-haze dataset and reports leading results on five real-world dehazing benchmarks.
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Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment
GACR reformulates cloud removal as an observation-anchored residual inversion process with geo-contextual prior alignment to preserve semantic structures for improved downstream interpretation tasks.
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RTE-FM-Dehazer: Radiative Transfer Equation Inspired Flow Matching for Real-World Image Dehazing
RTE-FM-Dehazer trains a flow-matching model with an RTE-derived diffusion-absorption regularizer on a new 50k real-haze dataset and reports leading results on five real-world dehazing benchmarks.