We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior distribution with respect to its estimate based on a prior contrast enhancement algorithm. We then design our low light enhancement and denoising (LLEAD) method by computing the minimum mean squared error estimate of the pristine image band pass coefficients. We create the Indian Institute of Science low light image dataset of well-lit and low light image pairs to learn the model parameters and evaluate our enhancement method. We show through extensive experiments on multiple datasets that our method helps better enhance the contrast while simultaneously controlling the noise when compared to other state of the art joint contrast enhancement and denoising methods.

我们使用统计方法研究联合低光图像对比度增强和去噪的问题。带通域中的低光自然图像通过将原始图像的高斯尺度混合模型通过细节损失系数和高斯噪声统计关联到低光图像来建模。细节损失系数使用后验分布对其基于先前对比度增强算法的估计进行统计描述。然后，我们通过计算原始图像带通系数的最小均方误差估计来设计我们的低光增强和去噪 (LLEAD) 方法。我们创建了印度科学研究所的光线充足和低光图像对的低光图像数据集，以学习模型参数并评估我们的增强方法。