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New AI methods improve low-light image and video enhancement

By PulseAugur Editorial · [8 sources] ·

Researchers have developed several new methods for enhancing low-light images and videos. One approach, PixIE, uses a vision foundation model to prompt pixel-space enhancement, improving detail recovery and reducing noise. Another method, InterLight, leverages intrinsic illumination priors and physics-guided augmentation to create an illumination-aware pipeline for clearer textures. Additionally, a new dataset called BVI-RLV has been released to address the scarcity of aligned training data for low-light video enhancement, which has shown significant performance gains when used for training models. AI

IMPACT These advancements offer improved visual quality and detail recovery in challenging lighting conditions, potentially benefiting applications like autonomous driving and surveillance.

RANK_REASON Multiple research papers detailing new methods and datasets for low-light image and video enhancement.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 8 sources. How we write summaries →

New AI methods improve low-light image and video enhancement

COVERAGE [8]

  1. arXiv cs.AI TIER_1 English(EN) · Shimon Murai, Teppei Kurita, Ryuta Satoh, Yusuke Moriuchi ·

    Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net

    arXiv:2604.11071v3 Announce Type: replace-cross Abstract: We present a lightweight two-stage framework for low-light image enhancement (LLIE) that achieves competitive perceptual quality with significantly fewer parameters than existing methods. Our approach combines frozen algor…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    InterLight: Leveraging Intrinsic Illumination Priors for Low-Light Image Enhancement

    Low-Light Image Enhancement (LLIE) has long been a challenging problem in low-level vision, as insufficient illumination often leads to low contrast, detail loss, and noise. Recent studies show that deep learning-based Retinex theory can effectively decouple illumination and refl…

  3. arXiv cs.CV TIER_1 English(EN) · Ruirui Lin, Guoxi Huang, David Bull, Nantheera Anantrasirichai ·

    PixIE: Prompted Pixel-Space Low-Light Image Enhancement

    arXiv:2605.23531v1 Announce Type: new Abstract: Low-light images exhibit severe noise, contrast loss, and semantic ambiguity, making enhancement a joint problem of denoising and detail recovery. We propose PixIE, a feed-forward pixel-space LLIE framework semantically-prompted by …

  4. arXiv cs.CV TIER_1 English(EN) · Ruirui Lin, Guoxi Huang, Nantheera Anantrasirichai ·

    Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise

    arXiv:2510.09450v2 Announce Type: replace Abstract: Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy real-world noise because they do not sufficient…

  5. arXiv cs.CV TIER_1 English(EN) · Ruirui Lin, Guoxi Huang, Joanne Lin, Qi Sun, Alexandra Malyugina, David R Bull, Nantheera Anantrasirichai ·

    BVI-RLV: A Fully Registered Dataset for Low-Light Video Enhancement

    arXiv:2407.03535v3 Announce Type: replace Abstract: Low-light videos often exhibit spatiotemporally incoherent noise, compromising visibility and degrading performance in computer vision applications. A major challenge for enhancing such content using deep learning lies in the sc…

  6. arXiv cs.CV TIER_1 English(EN) · Nantheera Anantrasirichai ·

    PixIE: Prompted Pixel-Space Low-Light Image Enhancement

    Low-light images exhibit severe noise, contrast loss, and semantic ambiguity, making enhancement a joint problem of denoising and detail recovery. We propose PixIE, a feed-forward pixel-space LLIE framework semantically-prompted by a vision foundation model. PixIE first performs …

  7. arXiv cs.CV TIER_1 English(EN) · Senyan Xu, Zhijing Sun, Kean Liu, Xin Lu, Ruixuan Jiang, Mingyang Huang, Xueyang Fu, Zheng-Jun Zha ·

    Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset

    arXiv:2605.22186v1 Announce Type: new Abstract: Event-based low-light image enhancement (LIE) methods mainly focus on incorporating high dynamic range (HDR) information from events while overlooking the essential global illumination in images and the inherent noise sensitivity of…

  8. arXiv cs.CV TIER_1 English(EN) · Huan Zhang ·

    InterLight: Leveraging Intrinsic Illumination Priors for Low-Light Image Enhancement

    Low-Light Image Enhancement (LLIE) has long been a challenging problem in low-level vision, as insufficient illumination often leads to low contrast, detail loss, and noise. Recent studies show that deep learning-based Retinex theory can effectively decouple illumination and refl…

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