A Novel Multi-Focus Image Fusion Network with U-Shape Structure

Sensors
Tao PanBin Tan

Abstract

Multi-focus image fusion has become a very practical image processing task. It uses multiple images focused on various depth planes to create an all-in-focus image. Although extensive studies have been produced, the performance of existing methods is still limited by the inaccurate detection of the focus regions for fusion. Therefore, in this paper, we proposed a novel U-shape network which can generate an accurate decision map for the multi-focus image fusion. The Siamese encoder of our U-shape network can preserve the low-level cues with rich spatial details and high-level semantic information from the source images separately. Moreover, we introduce the ResBlocks to expand the receptive field, which can enhance the ability of our network to distinguish between focus and defocus regions. Moreover, in the bridge stage between the encoder and decoder, the spatial pyramid pooling is adopted as a global perception fusion module to capture sufficient context information for the learning of the decision map. Finally, we use a hybrid loss that combines the binary cross-entropy loss and the structural similarity loss for supervision. Extensive experiments have demonstrated that the proposed method can achieve the state-of-the-art per...Continue Reading

References

Feb 2, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Shutao LiJianwen Hu
Jun 13, 2018·Neural Computation·Xiaopeng GuoWenhua Qian

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Software Mentioned

PyTorch
DeepFuse
ResBlock
Net
Adam
MWG
DSIFT

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