自适应权重细节保持多曝光图像融合  

Detail-Preserving Multi-Exposure Image Fusion Based on Adaptive Weight

作　　者：文瑞鸿 刘春雨[1,3] 刘帅 周美丽[1,3] 张玉鑫[1,3] Wen Ruihong;Liu Chunyu;Liu Shuai;Zhou Meili;Zhang Yuxin(Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,Jilin,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Space-based Dynamic&Rapid Optical Imaging Technology,Chinese Academy of Sciences,Changchun 130033,Jilin,China)

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [2]中国科学院大学,北京100049 [3]中国科学院天基动态快速光学成像技术重点实验室,吉林长春130033

出　　处：《激光与光电子学进展》2024年第18期316-325,共10页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62175236)。

摘　　要：多曝光图像融合针对图像传感器不足以捕获大动态范围场景的问题,将同一场景下多幅曝光程度不同的图像融合,得到一幅包含丰富场景细节的高动态范围图像。针对融合中常出现的图像细节保存不足、边缘光晕等现象,提出一种自适应权重细节保持多曝光图像融合算法。利用图像块分解中的对比度与结构分量提取融合结构度权重,同时利用二维熵选取亮度基准计算曝光度权重,结合饱和度权重使融合后的图像更好地还原场景的亮度与色彩信息。最后利用双金字塔融合在多个尺度上融合源图像序列,能够避免边界处不自然的光晕,得到保留更多细节的高动态范围融合图像。选取3个数据集中的70组多曝光图像进行实验,结果表明,所提算法的平均融合结构相似度达到0.983,平均交叉熵达到2.341,与经典或最新的多曝光融合方法相比能维持场景本身的亮度分布,同时保持更多的图像信息,验证了所提算法的有效性。所提算法融合结果优秀,视觉效果良好。Multi-exposure image fusion addresses the issue of insufficient image sensors for capturing scenes with large dynamic ranges.Multiple images with different exposure levels in the same scene are fused to obtain a large-dynamic-range image that contains rich scene details.A self-adaptive weight-detail-preserving multi-exposure image-fusion algorithm is proposed to address the typical issues of insufficient image-detail preservation and edge halo in fusion.Contrast and structural components in image-block decomposition are used to extract fused structural weights and two-dimensional entropy is used to select brightness benchmarks to calculate exposure weights.Subsequently,saturation weights are used to better restore the brightness and color information of the scene in the fused image.Finally,double-pyramid fusion is used to fuse the source-image sequence at multiple scales to avoid unnatural halos at the boundaries and obtain a largedynamic-range fused image that preserves more details.Seventy sets of multi-exposure images from three datasets are selected for experiments.The results show that the average values for the fusion-structure similarity and cross-entropy of the proposed algorithm are 0.983 and 2.341,respectively.Compared with classical or recent multi-exposure fusion algorithms,the proposed algorithm can maintain the brightness distribution of the scene while maintaining more image information,thus demonstrating its effectiveness.The proposed algorithm offers excellent fusion results and good visual effects.

关 键 词：图像处理 高动态范围 多曝光融合 图像块分解 二维熵 图像金字塔 

分 类 号：TN957.52[电子电信—信号与信息处理]