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FusionDiff: Multi-focus image fusion using denoising diffusion probabilistic models
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DOI：10.1016/j.eswa.2023.121664.png)
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Abstract
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Multi-focus image fusion (MFIF) is an image enhancement technology with broad application prospects that can effectively extend the depth-of-field of optical lenses. This paper presents a novel MFIF method, named FusionDiff, based on denoising diffusion probabilistic models (DDPM). FusionDiff uses DDPM to fuse two source images by iteratively performing multiple denoising operations. To predict noise accurately and train the model efficiently, a lightweight U-Net architecture is designed as the conditional noise predictor. FusionDiff does not depend on any specific activity level measurement method, fusion rule or complex feature extraction network. It overcomes many algorithm design and training difficulties in existing image fusion methods. FusionDiff is noise-resistant and can still produce outstanding fusion results from source images with noise interference. In addition, FusionDiff is a few-shot learning method, which makes it suitable for image fusion tasks where training samples are relatively scarce. Experiments show that FusionDiff outperforms representative state-of-the-art methods in both visual perception and quantitative metrics. The code is available at https://github.com/lmn-ning/ImageFusionhttps://github.com/lmn-ning/ImageFusion
Keywords:
Multi-focus image fusion
Denoising diffusion probabilistic models
Few-shot learning
Noise-robust method
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