基于质量度量与颜色校正的多曝光图像融合算法

针对当前多曝光图像融合过程的图像质量属性选择不当,易导致颜色失真与丢失细节等问题,设计了一种质量度量耦合颜色校正的多曝光图像融合算法。首先,选择3种最显著的图像质量属性(对比度、饱和度和亮度)作为度量方式;然后,利用线性组合将得到的3种质量属性进行成加权组合,并采用幂函数来控制每个属性的影响,将较低的权重值赋予曝光不足和曝光过度的像素,消除具有视觉效果不佳的像素,从而有效保留正常的曝光像素及其明亮的颜色与细节;随后,将不同曝光图像加权组合的特征进行Laplacian金字塔分解,经过归一化权重映射后,对其进行不同系数的多分尺度融合,完成多曝光图像融合。为了避免颜色失真与细节丢失,采用基于局部饱和度的颜色校正方法来改善图像质量。实验数据表明,与当前多曝光图像融合方案相比,所提算法具有更高的融合视觉质量,可以更好地保持图像细节和校正曝光融合图像的颜色。

Multi exposure image fusion algorithm based on quality metric coupled with color correction

Aiming at the problems of color distortion and loss of detail caused by improper selection of image quality attributes in the current multi exposure image fusion process, a multi exposure image fusion scheme based on quality measurement coupled with color correction was designed. Firstly, three most prominent image quality attributes (contrast, saturation and brightness) were selected as measurement methods. Secondly, these three quality attributes were weighted by linear combination, and the power function was used to control the influence of each attribute. Low weight values are assigned to underexposed and overexposed pixels to eliminate pixels with poor visual effects, thus, effectively preserving exposure pixels, bright colors and details. Then, Laplacian pyramid decomposition is used to decompose the weighted combination features of different exposure images. After normalized weight mapping, multi resolution fusion of different coefficients was performed to achieve multi exposure image fusion. In addition, in order to avoid color distortion and detail loss, the post processing steps of color correction based on local saturation are adopted to improve image quality. Experimental results show that the proposed algorithm has higher fusion visual quality than current multi exposure image fusion scheme, and can better maintain image details and correct the color of the exposure fusion image.