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ARXIV:2603.15131 · IMAGE ENHANCEMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15131IMAGE ENHANCEMENTSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques.
Opportunity summary
Pain A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components.
Retinex theory provides a principled foundation for low-light image enhancement, inspiring numerous learning-based methods that integrate its principles. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experimental evaluations across four benchmark datasets validate that our method achieves competitive performance in low-light enhancement and a more stable training process.
Image Enhancement moved forward this cycle; last verified April 2026. Public score 6.0/10.
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A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques.
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10.48550/arXiv.2603.15131A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques.
Abstract
Retinex theory provides a principled foundation for low-light image enhancement, inspiring numerous learning-based methods that integrate its principles. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components. To address this, we propose a Retinex-Guided Transformer~(RGT) model, which is a two-stage model consisting of decomposition and enhancement phases. First, we propose a latent space decomposition strategy to separate reflectance and illumination components. By incorporating the log transformation and 1-pixel offset, we convert the intrinsically multiplicative relationship into an additive formulation, enhancing decomposition stability and precision. Subsequently, we construct a U-shaped component refiner incorporating the proposed guidance fusion transformer block. The component refiner refines reflectance component to preserve texture details and optimize illumination distribution, effectively transforming low-light inputs to normal-light counterparts. Experimental evaluations across four benchmark datasets validate that our method achieves competitive performance in low-light enhancement and a more stable training process.
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PROBLEM
A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components.
METHOD
Retinex theory provides a principled foundation for low-light image enhancement, inspiring numerous learning-based methods that integrate its principles. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experimental evaluations across four benchmark datasets validate that our method achieves competitive performance in low-light enhancement and a more stable training process.
WHY NOW
Image Enhancement moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Retinex theory provides a principled foundation for low-light image enhancement, inspiring numerous learning-based methods that integrate its principles. However, existing methods exhibits limitations in accurately decomposing reflectance and illumination components.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Experimental evaluations across four benchmark datasets validate that our method achieves competitive performance in low-light enhancement and a more stable training process.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Image Enhancement moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel Retinex-Guided Transformer model for stable low-light image enhancement through advanced decomposition techniques.
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Image Enhancement
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