Image Dehazing as In-Context Tabular Inference
Image dehazing is traditionally addressed through handcrafted physical priors or task-specific deep learning models, often requiring carefully designed architectures or extensive model training. Recent advances in tabular foundation models (TFMs), however, offer an alternative learning paradigm in which continuous relationships can be inferred directly from contextual examples without task-specific model training. Motivated by this capability, this paper investigates whether single-image dehazing can be effectively reformulated as an in-context tabular regression problem and introduces DehazeTab, a framework that estimates atmospheric transmission from structured image-patch representatio...
| Authors | Le-Anh Tran |
|---|---|
| Primary category | cs.CV |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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