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Pyramid Diffusion Models For Low-light Image Enhancement

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arxiv 2305.10028 v1 pith:42HJPOWC submitted 2023-05-17 cs.CV

Dewei Zhou , Zongxin Yang , Yi Yang This is my paper · ORCID
classification cs.CV
keywords diffusionmodelspydiffpyramidimagelow-lightdegradationenhancement
verification ladder T0 review T1 audit T2 compute T3 formal
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Recovering noise-covered details from low-light images is challenging, and the results given by previous methods leave room for improvement. Recent diffusion models show realistic and detailed image generation through a sequence of denoising refinements and motivate us to introduce them to low-light image enhancement for recovering realistic details. However, we found two problems when doing this, i.e., 1) diffusion models keep constant resolution in one reverse process, which limits the speed; 2) diffusion models sometimes result in global degradation (e.g., RGB shift). To address the above problems, this paper proposes a Pyramid Diffusion model (PyDiff) for low-light image enhancement. PyDiff uses a novel pyramid diffusion method to perform sampling in a pyramid resolution style (i.e., progressively increasing resolution in one reverse process). Pyramid diffusion makes PyDiff much faster than vanilla diffusion models and introduces no performance degradation. Furthermore, PyDiff uses a global corrector to alleviate the global degradation that may occur in the reverse process, significantly improving the performance and making the training of diffusion models easier with little additional computational consumption. Extensive experiments on popular benchmarks show that PyDiff achieves superior performance and efficiency. Moreover, PyDiff can generalize well to unseen noise and illumination distributions.

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Cited by 5 Pith papers

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    A latent-cascaded video generation framework with dual frequency-split experts reports state-of-the-art 2K/4K video generation on VBench, FIDpatch, and human preference.

  2. Diffusion-Guided Knowledge Distillation for Weakly-Supervised Low-Light Semantic Segmentation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A diffusion-based knowledge distillation framework with depth-guided feature fusion improves weakly-supervised semantic segmentation in low-light images, achieving state-of-the-art results on dark PASCAL VOC and the L...

  3. HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement

    cs.CV 2025-07 conditional novelty 5.0 of 10

    HVI-CIDNet+ replaces the HSV color plane with polarized hue-saturation coordinates and a learned dark-intensity collapse, then trains a dual-branch transformer-CNN network with CLIP-derived priors for low-light enhancement.

  4. UBLLIE: Unified Backlight and Low-Light Image Enhancement

    cs.CV 2026-08 conditional novelty 4.0 of 10

    A unified, unsupervised CLIP-prompt-guided enhancement network with a residual U-Net and ASPP outperforms prior methods on backlit and low-light benchmarks.

  5. Low-light Image Enhancement via Multi-scale Attention combined with Fourier Transform

    cs.CV 2026-07 reject novelty 4.0 of 10

    A Fourier-amplitude-guided multi-scale attention network is claimed to outperform prior low-light enhancement methods by wide margins on LOL, SID, SMID, and SDSD benchmarks.

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