Liu Yu, Qi Zhengzheng, Cheng Juan, Chen Xun
IEEE Trans Pattern Anal Mach Intell. 2024 Aug;46(8):5806-5819. doi: 10.1109/TPAMI.2024.3367905. Epub 2024 Jul 2.
As an effective technique to extend the depth-of-field (DOF) of optical lenses, multi-focus image fusion has recently become an active topic in image processing community. However, a major problem remaining unsolved in this field is the lack of universal criteria in selecting objective evaluation metrics. Consequently, the metrics utilized in different studies often vary significantly, leading to high difficulties in achieving unbiased evaluation. To address this problem, this paper proposes a statistic-based approach for verifying the effectiveness of objective metrics in multi-focus image fusion. The core idea is to adopt statistical correlation measures to evaluate the performance consistency between a certain fusion metric and some popular full-reference image quality assessment models. In addition, a convolutional neural network (CNN)-based fusion metric is presented to measure the similarity between the source images and the fused image based on the semantic features at multiple abstraction levels. A comparative study is conducted to evaluate 20 existing fusion metrics using the proposed statistic-based approach on a large-scale, realistic and with-ground-truth multi-focus image fusion dataset recently released. Experimental results demonstrate the feasibility of the proposed approach in evaluating the effectiveness of objective metrics and the advantage of our CNN-based metric.
作为一种扩展光学镜头景深（DOF）的有效技术，多聚焦图像融合最近已成为图像处理领域的一个活跃话题。然而，该领域仍未解决的一个主要问题是在选择客观评估指标时缺乏通用标准。因此，不同研究中使用的指标往往差异很大，导致难以实现无偏评估。为了解决这个问题，本文提出了一种基于统计的方法来验证多聚焦图像融合中客观指标的有效性。核心思想是采用统计相关性度量来评估特定融合指标与一些流行的全参考图像质量评估模型之间的性能一致性。此外，还提出了一种基于卷积神经网络（CNN）的融合指标，以基于多个抽象层次的语义特征来衡量源图像与融合图像之间的相似性。利用最近发布的一个大规模、真实且有地面真值的多聚焦图像融合数据集，采用所提出的基于统计的方法对20种现有的融合指标进行了比较研究。实验结果证明了所提方法在评估客观指标有效性方面的可行性以及基于CNN的指标的优势。
