Wei Bingzhe, Feng Xiangchu, Wang Kun, Gao Bian
School of Mathematics and Statistics, Xidian University, Xi'an 710071, China.
Entropy (Basel). 2021 Jun 28;23(7):827. doi: 10.3390/e23070827.
Multi-focus-image-fusion is a crucial embranchment of image processing. Many methods have been developed from different perspectives to solve this problem. Among them, the sparse representation (SR)-based and convolutional neural network (CNN)-based fusion methods have been widely used. Fusing the source image patches, the SR-based model is essentially a local method with a nonlinear fusion rule. On the other hand, the direct mapping between the source images follows the decision map which is learned via CNN. The fusion is a global one with a linear fusion rule. Combining the advantages of the above two methods, a novel fusion method that applies CNN to assist SR is proposed for the purpose of gaining a fused image with more precise and abundant information. In the proposed method, source image patches were fused based on SR and the new weight obtained by CNN. Experimental results demonstrate that the proposed method clearly outperforms existing state-of-the-art methods in addition to SR and CNN in terms of both visual perception and objective evaluation metrics, and the computational complexity is greatly reduced. Experimental results demonstrate that the proposed method not only clearly outperforms the SR and CNN methods in terms of visual perception and objective evaluation indicators, but is also significantly better than other state-of-the-art methods since our computational complexity is greatly reduced.
多聚焦图像融合是图像处理的一个关键分支。人们从不同角度开发了许多方法来解决这个问题。其中，基于稀疏表示（SR）和基于卷积神经网络（CNN）的融合方法得到了广泛应用。基于SR的模型通过融合源图像块，本质上是一种具有非线性融合规则的局部方法。另一方面，源图像之间的直接映射遵循通过CNN学习得到的决策图。这种融合是一种具有线性融合规则的全局融合。为了获得具有更精确和丰富信息的融合图像，结合上述两种方法的优点，提出了一种应用CNN辅助SR的新型融合方法。在所提出的方法中，基于SR和由CNN获得的新权重对源图像块进行融合。实验结果表明，所提出的方法在视觉感知和客观评估指标方面，除了SR和CNN之外，明显优于现有的最先进方法，并且计算复杂度大大降低。实验结果表明，所提出的方法不仅在视觉感知和客观评估指标方面明显优于SR和CNN方法，而且由于计算复杂度大大降低，也明显优于其他最先进方法。
