Muti-focus image fusion is the extraction of focused regions from different images to create one all-in-focus fused image. The key point is that only objects within the depth-of-field have a sharp appearance in the photograph, while other objects are likely to be blurred. We propose an unsupervised deep learning model for multi-focus image fusion. We train an encoder–decoder network in an unsupervised manner to acquire deep features of input images. Then, we utilize spatial frequency, a gradient-based method to measure sharp variation from these deep features, to reflect activity levels. We apply some consistency verification methods to adjust the decision map and draw out the fused result. Our method analyzes sharp appearances in deep features instead of original images, which can be seen as another success story of unsupervised learning in image processing. Experimental results demonstrate that the proposed method achieves state-of-the-art fusion performance compared to 16 fusion methods in objective and subjective assessments, especially in gradient-based fusion metrics.

多焦点图像融合是从不同图像中提取聚焦区域，以创建一个全焦点融合图像。关键点在于，只有景深内的物体才能在照片中呈现清晰的外观，而其他物体则很可能模糊。我们提出了一种用于多焦点图像融合的无监督深度学习模型。我们以无监督的方式训练编码器-解码器网络，以获取输入图像的深层特征。然后，我们利用空间频率（一种基于梯度的方法）来测量这些深层特征的急剧变化，以反映活动水平。我们应用了一些一致性验证方法来调整决策图并得出融合结果。我们的方法可以分析深层特征中的鲜明外观，而不是原始图像，这可以看作是图像处理中无监督学习的另一个成功案例。实验结果表明，与16种融合方法相比，在主观和主观评估中，尤其是在基于梯度的融合指标中，该方法可实现最新的融合性能。