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.
In: International conference on advanced concepts for intelligent vision systems
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SEAR introduces a dual-process agentic framework for image restoration that combines pruning-aware MCTS planning with self-evolving episodic memory to address greedy search and episodic amnesia limitations.
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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.
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Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution
SEAR introduces a dual-process agentic framework for image restoration that combines pruning-aware MCTS planning with self-evolving episodic memory to address greedy search and episodic amnesia limitations.