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TPP: Deep learning based threshold post-processing multi-focus image fusion method
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DOI：10.1016/j.compeleceng.2023.108736.png)
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Abstract
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How to accurately determine focused and defocused domains is a difficult problem in the field of multi-focused image fusion. To solve this problem, we propose a new deep learning method called threshold post processing(TPP). The TPP network model contains three architecturally different channels, and three different weighted maps can be obtained for the same input. The output of the model is fused and thresholded to obtain a final decision map, which is then used to complete the image fusion task. The decision map represents the focus condition of the source image at a pixel level. This allows TPP to retain focused regions of the source image more accurately than other existing deep learning methods. The multi-channel network model combined with the thresh-olding process optimizes the final decision map. It avoids the drawback of other methods that cause inaccurate discrimination of the focused area due to a single network channel and therefore cannot be corrected. Subsequent experimental results show that the images generated by TPP maintain a high level under the various image quality assessment metrics measures.
Keywords:
Multi-focus
Image fusion
Convolution neural network
Supervised learning
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