结合条件过分散噪声分析的亮通道Retinex增强
Bright-Channel Retinex Enhancement with a Conditional Overdispered-Noise Analysis
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中文总结 AI 辅助
提出一种无需训练的低光照增强方法，结合亮通道Retinex与条件过分散噪声分析，在LOL-v1数据集上获传统方法最高PSNR/SSIM，处理速度达43 FPS。
中文摘要 AI 辅助
本文提出一种无需训练的低光照增强方法，结合局部亮通道照度估计、Retinex分解及保边去噪。对于固定照度估计，采用条件负二项伪计数法表征分解放大的异方差噪声；无约束反射率比为逐像素最大似然估计，对零值观测设边界解，实现时额外应用照度滤波与范围裁剪。该负二项模型为诊断性噪声分析而非校准传感器模型，最终固定带宽双边滤波为经验近似而非精确贝叶斯解。在LOL-v1数据集上，该方法取得17.74dB/0.739的平均PSNR/SSIM，为传统方法评估中的最高值；在Apple M2 Pro CPU上处理400×600图像的速度约为43 FPS。
英文摘要
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 psueduo-count method characterises the heteroscedastic noise amplified by division. The unconstrained reflectance ratio is the pixelwise maximum-likelihood estimate, with a boundary solution for zero-valued observations; the implemented estimate additionally applies illumination filtering and range clipping. The NB model is a diagnostic noise analysis rather than a calibrated sensor model, and the final fixed-bandwidth bilateral filter is an empirical approximation rather than the exact Bayesian solution. On the LOL-v1 dataset, the methodobtains mean PSNR/SSIM of 17.74dB/0.739, the highest values among the evaluated with conventional methods. A 400X600 image is processed at approximately 43 FPS on an Apple M2 Pro CPU.
发表机构
- Kyushu Institute of Technology（九州工业大学）
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