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Consist-Retinex accelerates high-quality low-light image enhancement with one-step training

Researchers have developed Consist-Retinex, a novel method for enhancing low-light images by separating reflectance and illumination components. This approach utilizes a Retinex Transformer Decomposition Network and trains conditional consistency models with a dual objective that combines trajectory consistency and component alignment. The method focuses supervision near the inference endpoint to improve stability and quality in one-step restoration, outperforming existing techniques on specific benchmarks. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a more efficient and stable method for low-light image enhancement, potentially improving performance in real-time applications.

RANK_REASON This is a research paper detailing a new method for image enhancement.

Read on arXiv cs.CV →

COVERAGE [1]

  1. arXiv cs.CV TIER_1 · Jian Xu, Wei Chen, Shigui Li, Delu Zeng, John Paisley, Qibin Zhao ·

    Consist-Retinex: One-Step Noise-Emphasized Consistency Training Accelerates High-Quality Retinex Enhancement

    arXiv:2512.08982v2 Announce Type: replace Abstract: Retinex-based low-light image enhancement benefits from separating reflectance and illumination, yet recent generative approaches often rely on iterative sampling and are difficult to deploy under strict latency budgets. Consist…