基于模拟多曝光融合的低照度全景图像增强

针对低照度全景图像存在的对比度低、视觉效果差等问题,提出了一种基于模拟多曝光融合的低照度全景图像增强算法。首先,将原图像从RGB颜色空间转换到HSV颜色空间,以图像信息熵作为度量估计最佳曝光率,采用亮度映射函数对V分量进行增强处理,再将其转回RGB颜色空间得到过曝光图像;接着,以低照度图像和过曝光图像为输入,采用曝光插值法合成中等曝光图像;然后,采用多尺度融合策略将低照度图像、中等曝光图像和过曝光图像进行融合,得到融合后的图像;最后,通过多尺度细节增强算法对融合后的图像进行细节增强,得到最终的增强图像。通过与NPE,LIME,SRIE,Li,Ying,RtinexNet算法相比,在不同场景的全景图像上,亮度顺序误差(LOE)最小为322,自然图像质量评估器(NIQE)最小为2.32,无参考空间域图像质量评估器最小为5.71,结构相似度(SSIM)最高达到0.82,综合性能优于其他对比算法。实验结果表明,本文算法能够有效地提升低照度全景图像的质量。

Low illumination panoramic image enhancement algorithm based on simulated multi-exposure fusion

Panoramic images captured under low-illumination conditions suffer from low contrast and poor visual effects.To address these problems,we propose a low-illumination panoramic image enhancement algorithm based on simulated multi-exposure fusion.First,the original image is converted to HSV color space;then,the optimal exposure rate is estimated by using a metric of image information entropy,and the V component is enhanced by using an intensity transform function to obtain an overexposed image.Second,a medium-exposure image is generated by using an exposure interpolation method,which utilizes the low-light image and overexposed image as input.Third,the fused image is obtained by employing a multi-fusion strategy in which the original low-illumination image,medium-exposure image,and overexposed image are fused.Finally,the detailed information is enhanced by using a multi-scale detail boosting method.The proposed method exhibits better performance compared with NPE,LIME,SRIE,Li,Ying,and RtinexNet algorithms.In case of panoramic images of different scenes,the lightness order error is 322,natural image quality evaluator is 2.32,blind/referenceless image spatial quality evaluator is 5.71,and structure similarity index is 0.82.The comprehensive performance of the proposed method is found to be better than that of other comparison algorithms.Experimental results show that the quality of the low-illumination panoramic image can be improved effectively by using the proposed algorithm.