利用自适应光照初始化的弱光图像增强方法

Low Light Image Enhancement With Adaptive Light Initialization

由于光照分量分解估计的高度不确定性,如何准确估计图像的光照分量一直是基于Retinex模型的图像增强方法需要解决的难题。该文提出一个简单有效的方法,准确估计图像的初始光照分量,进而实现弱光图像增强。具体地,首先根据输入图像得到其对应的光照权重矩阵,以指导光照分量的自适应初始化估计;随后在光照结构约束下,对初始光照分量优化估计,并进一步执行非线性光照调整;最终结合Retinex模型得到增强结果。实验表明,该方法不仅能够实现准确的图像分解估计,而且与现有的弱光图像增强方法相比,该文所提方法在多个数据集上的主观视觉效果和客观评价指标都有更好的表现,同时也保持着良好的运行效率。

Due to the high uncertainty in the estimation of the light component decomposition,how to accurately estimate the light component of an image has been a challenge to be addressed by image enhancement methods based on the Retinex model.An effective method is proposed to accurately estimate the initial illumination component in this paper.Specifically,the corresponding illumination weight matrices for different inputs are obtained to guide the adaptive initialization estimation,subsequently the estimation of the initial illumination components are optimized under the constraints of the illumination structure,and the nonlinear illumination adjustment be performed on them.Finally,the Retinex be combined to obtain the enhanced images.Experiments show that our method not only achieves accurate image decomposition estimation,but also performs better in terms of both subjective visual effects and objective evaluation metrics on multiple datasets while maintaining good operational efficiency compared with existing methods for low-light image enhancement.