基于亮度评估技术的特征增强衍生图融合算法

Feature Enhancement Derivative Fusion Algorithm Based on Luminance Evaluation Technology

针对由动态范围,光照条件,图像捕获设备等因素获得的低亮度图像,提出了一种基于亮度评估技术的特征增强衍生图融合算法来实现亮度较暗图像的对比度调整和特征增强.首先,利用亮度评估技术对低亮度图像的亮度进行评估优化处理,得到曝光率映射;然后,结合曝光率映射和改进的卡方分布函数模型来获取两幅特征增强的衍生图进行融合.最后,利用改进的衍生图融合算法得到最终融合图像.实验结果表明,所提算法的亮度误差,视觉信息保真度,图像互信息等评估参数优于近期方法,在提升图像对比度同时保留了图像良好曝光率区域,并较好地恢复了低亮度区域的边缘以及纹理等细节信息.

Focused on the low-light images obtained from dynamic range, illumination condition, image acquisition equipment, etc., a feature enhancement derivative fusion algorithm based on luminance evaluation technology was proposed to achieve contrast adjustment and feature enhancement of the low-light images. Firstly, the brightness evaluation technique was used to optimize the brightness of the low-light image to obtain the exposure ratio map. Then,combining exposure ratio map and improved chi-square distribution function model, two derivatives with enhanced features were obtained for fusion. Finally, the fusion image was obtained by using the improved derivative fusion algorithm. The experimental results indicate that the proposed algorithm achieves the better results including brightness order error, visual information fidelity and image mutual information, improves the image contrast while preserving the well-exposed region, and it can recover the edge and texture details of the low-luminance region.