Abstract Most existing sparse representation-based (SR) fusion methods consider the local information of each image patch independently during fusion. Some spatial artifacts are easily introduced to the fused image. A sliding window technology is often employed by these methods to overcome this issue. However, this comes at the cost of high computational complexity. Alternatively, we come up with a novel multi-focus image fusion method that takes full consideration of the strong correlations among spatially adjacent image patches with NO need for a sliding window. To this end, a non-negative SR model with local consistency constraint (CNNSR) on the representation coefficients is first constructed to encode each image patch. Then a patch-level consistency rectification strategy is presented to merge the input image patches, by which the spatial artifacts in the fused images are greatly reduced. As well, a compact non-negative dictionary is constructed for the CNNSR model. Experimental results demonstrate that the proposed fusion method outperforms some state-of-the art methods. Moreover, the proposed method is computationally efficient, thereby facilitating real-world applications.

中文翻译：

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基于非负稀疏表示和补丁级一致性校正的多焦点图像融合

摘要 大多数现有的基于稀疏表示（SR）的融合方法在融合过程中独立考虑每个图像块的局部信息。一些空间伪影很容易被引入到融合图像中。这些方法通常采用滑动窗口技术来解决这个问题。然而，这是以高计算复杂性为代价的。或者，我们提出了一种新颖的多焦点图像融合方法，该方法充分考虑了空间相邻图像块之间的强相关性，而无需滑动窗口。为此，首先构建对表示系数具有局部一致性约束（CNNSR）的非负 SR 模型来对每个图像块进行编码。然后提出了补丁级一致性校正策略来合并输入图像补丁，从而大大减少了融合图像中的空间伪影。同样，为 CNNSR 模型构建了一个紧凑的非负字典。实验结果表明，所提出的融合方法优于一些最先进的方法。此外，所提出的方法在计算上是高效的，从而促进了实际应用。