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This paper presents a novel convolutional neural network-based single-pixel imaging method that integrates a physics-driven fusion attention mechanism. By incorporating a module combining both channel and spatial attention mechanisms into a randomly initialized convolutional network, the method utilizes the physical model constraints of single-pixel imaging to achieve high-quality image reconstruction. Specifically, the spatial and channel attention mechanisms are combined into a single module and introduced into various layers of a multi-scale U-net convolutional network. In the spatial attention mechanism, we extract the attention weight features of each spatial region of the pooled feature map using convolution. In the channel attention mechanism, we pool the three-dimensional feature map into a single-channel signal and input it into a two-layer fully connected network to obtain the attention weight information for each channel. This approach not only leverages the critical weighting information provided by the attention mechanism in the three-dimensional data cube but also fully integrates the powerful feature extraction capabilities of the U-net network across different spatial frequencies. This innovative method effectively captures image details, suppresses background noise, and improves image reconstruction quality. During the experimental phase, we employed the optical path of single-pixel imaging to acquire bucket signals for two target images, ”snowflake” and ”basket”. By inputting any noise image into a randomly initialized neural network with an attention mechanism, and leveraging the mean square error between simulated and actual bucket signals, we physically constrained the network’s convergence. Ultimately, we achieved a reconstructed image that adhered to the physical model. Experimental results demonstrate that, under low sampling rate conditions, the scheme that integrates the attention mechanism not only intuitively reconstructs image details better but also demonstrates significant advantages in quantitative evaluation metrics such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), verifying its effectiveness and potential for application in singlepixel imaging.
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Keywords:
- Single-pixel imaging /
- attention mechanisms /
- convolutional neural networks /
- image reconstruction
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