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English(EN) Bright-Channel Retinex Enhancement with a Conditional Overdispered-Noise Analysis

新的低光图像增强方法实现高PSNR/SSIM

作者 PulseAugur 编辑部 · [1 个来源] ·

一种新的无训练低光图像增强方法已被开发出来，结合了局部光照估计、Retinex分解和边缘保持去噪。该方法利用条件负二项伪计数方法来表征分解过程中放大的异方差噪声，从而得到无约束的反射率比估计。该方法在LOL-v1数据集上实现了17.74dB/0.739的平均PSNR/SSIM，优于传统技术。此外，它可以在Apple M2 Pro CPU上以大约43 FPS的速度处理400x600的图像。 AI

影响 该方法为低光照条件下需要高质量图像处理的应用提供了潜在的改进。

排序理由 该项目是一篇学术论文，详细介绍了一种新颖的图像增强方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的低光图像增强方法实现高PSNR/SSIM

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该项目是一篇学术论文，详细介绍了一种新颖的图像增强方法。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jongpil Jeong ·

    基于条件过分散布噪声分析的亮通道Retinex增强

    arXiv:2608.09137v1 Announce Type: new Abstract: I present a training-free low-light enhancement method that combines local bright-channel illumination estimation, Retinex division, and edge-preserving denoising. For a fixed illumination estimate, a conditional Negative -Binominal…

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