Single-pixel imaging (SPI) is a potential computational imaging technique
which produces image by solving an illposed reconstruction problem from few
measurements captured by a single-pixel detector. Deep learning has achieved
impressive success on SPI reconstruction. However, previous poor reconstruction
performance and impractical imaging model limit its real-world applications. In
this paper, we propose a deep unfolding network with hybrid-attention
Transformer on Kronecker SPI model, dubbed HATNet, to improve the imaging
quality of real SPI cameras. Specifically, we unfold the computation graph of
the iterative shrinkagethresholding algorithm (ISTA) into two alternative
modules: efficient tensor gradient descent and hybrid-attention multiscale
denoising. By virtue of Kronecker SPI, the gradient descent module can avoid
high computational overheads rooted in previous gradient descent modules based
on vectorized SPI. The denoising module is an encoder-decoder architecture
powered by dual-scale spatial attention for high- and low-frequency aggregation
and channel attention for global information recalibration. Moreover, we build
a SPI prototype to verify the effectiveness of the proposed method. Extensive
experiments on synthetic and real data demonstrate that our method achieves the
state-of-the-art performance. The source code and pre-trained models are
available at this https URL

通过使用深度展开网络和混合注意力 Transformer 解决 Kronecker SPI 模型的问题，我们提出了一种名为 HATNet 的方法，可以改善真实 SPI 相机的成像质量，并在合成和真实数据上进行了广泛实验证明方法达到了最先进的性能。