无需训练的低光增强方法，结合亮度通道估计与去噪，性能优于传统方法。
Bright-Channel Retinex Enhancement with a Conditional Overdispered-Noise Analysis
- 基于局部亮度通道估计和Retinex除法，实现无训练低光增强
- 在LOL-v1数据集上达到17.74dB PSNR/0.739 SSIM，为传统方法最高
- 运行速度快，400×600图像约43帧/秒，适合实时应用
本文提出一种无需训练的低光增强方法，结合局部亮度通道光照估计、Retinex除法和边缘保持去噪。针对固定的光照估计，采用条件负二项伪计数方法分析除法放大后的异方差噪声。反射率比率为像素级最大似然估计，对零观测值采用边界解；实际实现中还引入光照滤波与范围裁剪。该负二项模型用于噪声诊断而非校准传感器模型，最终的固定带宽双边滤波器是经验近似，非精确贝叶斯解。在LOL-v1数据集上，该方法获得17.74dB均值PSNR和0.739均值SSIM，为所评估传统方法中的最高值。400×600图像在Apple M2 Pro CPU上处理速度约为43 FPS。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。