Xu Ke, Wang Qin, Xiao Huangqing, Liu Kelin
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China.
Front Neurorobot. 2022 Mar 8;16:846580. doi: 10.3389/fnbot.2022.846580. eCollection 2022.
High-dynamic-range (HDR) image has a wide range of applications, but its access is limited. Multi-exposure image fusion techniques have been widely concerned because they can obtain images similar to HDR images. In order to solve the detail loss of multi-exposure image fusion (MEF) in image reconstruction process, exposure moderate evaluation and relative brightness are used as joint weight functions. On the basis of the existing Laplacian pyramid fusion algorithm, the improved weight function can capture the more accurate image details, thereby making the fused image more detailed. In 20 sets of multi-exposure image sequences, six multi-exposure image fusion methods are compared in both subjective and objective aspects. Both qualitative and quantitative performance analysis of experimental results confirm that the proposed multi-scale decomposition image fusion method can produce high-quality HDR images.
高动态范围(HDR)图像有广泛的应用,但对其访问有限。多曝光图像融合技术因其能获得类似于HDR图像的图像而受到广泛关注。为了解决多曝光图像融合(MEF)在图像重建过程中的细节损失问题,将曝光适度评估和相对亮度用作联合权重函数。在现有的拉普拉斯金字塔融合算法基础上,改进后的权重函数能够捕捉到更精确的图像细节,从而使融合后的图像更加详细。在20组多曝光图像序列中,从主观和客观两个方面对六种多曝光图像融合方法进行了比较。实验结果的定性和定量性能分析均证实,所提出的多尺度分解图像融合方法能够生成高质量的HDR图像。