基于多尺度梯度域引导滤波的低照度图像增强算法

针对低照度彩色图像整体亮度较低,增强图像中颜色易失真,部分图像细节淹没在较低灰度值像素中等问题,提出一种改进的低照度图像增强算法。首先,把待处理图像转换到色调、饱和度、亮度(HSI)颜色空间,对亮度分量进行非线性全局亮度校正;然后,提出多尺度梯度域引导滤波的亮度增强模型,利用该模型对校正后的亮度分量进行增强,接着对增强后的亮度分量进一步实施避免颜色失真的亮度校正;最后,将图像再转换回红绿蓝(RGB)颜色空间。实验结果表明,增强后的图像亮度平均提高90.0%以上,清晰度平均提高123.8%以上,这主要得益于多尺度梯度域引导滤波具有更好的亮度平滑和增强能力;同时由于减小了颜色失真,使增强图像的细节表现能力平均提高18.2%以上;由于采用了多尺度梯度域引导滤波的亮度增强模型与直方图自适应的亮度校正算法,使提出的低照度图像增强算法适宜应用于夜间等弱光源条件下的彩色图像增强。

Low-illumination image enhancement algorithm based on multi-scale gradient domain guided filtering

An improved low-illumination image enhancement algorithm was proposed to solve the problems that the overall intensity of low-illumination color image is low, the color in the enhanced image is easy to be distorted, and some enhanced image details are drowned in the pixels with low gray value. Firstly, an image to be processed was converted to the Hue Saturation Intensity (HSI) color space, and the nonlinear global intensity correction was carried out for the intensity component. Then, an intensity enhancement model based on multi-scale guided gradient domain filtering was put forward to enhance the corrected intensity component, and the intensity correction was further performed to avoid color distortion. Finally, the image was converted back into Red Green Blue (RGB) color space. Experimental results show that the enhanced images have the intensity increased by more than 90.0% on average, and the sharpness increased by more than 123.8% on average, which are mainly due to the better intensity smoothing and enhancement ability of multi-scale gradient domain guided filtering. At the same time, due to the reduction of color distortion, the detail performance of enhanced images increases by more than 18.2% on average. The proposed low-illumination image enhancement algorithm is suitable for enhancing color images under night and other weak light source conditions, because of using intensity enhancement model based on multi-scale gradient domain guided filtering and histogram adaptive intensity correction algorithm.