Deep learning methods have achieved remarkable success in image compressed sensing (CS) task, namely reconstructing a high-fidelity image from its compressed measurement. However, existing methods are deficient in incoherent compressed measurement at sensing phase and implicit measurement representations at reconstruction phase, limiting the overall performance. In this work, we answer two questions: (i) how to improve the measurement incoherence for decreasing the ill-posedness; (ii) how to learn informative representations from measurements. To this end, we propose a novel asymmetric Kronecker CS (AKCS) model and theoretically present its better incoherence than previous Kronecker CS with minimal increase of complexity. Moreover, apart from the explicit measurement representations in gradient descent projection in unfolding networks, we further propose a measurement-aware cross attention (MACA) mechanism to learn implicit measurement representations. We integrate AKCS and MACA into a widely-used unfolding architecture to get a measurement-enhanced unfolding network (MEUNet). Extensive experiments demonstrate that the proposed MEUNet achieves state-of-the-art (SOTA) performance in reconstruction accuracy with high efficiency.

Single-pixel imaging (SPI) is a potential computational imaging technique which produces image by solving an ill-posed reconstruction problem from few measurements captured by a single-pixel detector. Deep learning has achieved impressive success on SPI reconstruction. However, previ-ous 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 im-prove the imaging quality of real SPI cameras. Specifically, we unfold the computation graph of the iterative shrinkage-thresholding algorithm (ISTA) into two alternative modules: efficient tensor gradient descent and hybrid-attention multi-scale denoising. By virtue of Kronecker SPI, the gradient descent module can avoid high computational overheads rooted in previous gradient descent modules based on vector-ized SPI. The denoising module is an encoder-decoder archi-tecture powered by dual-scale spatial attention for high- and low-frequency aggregation and channel attention for global information recalibration. Moreover, we build a SPI proto-type to verify the effectiveness of the proposed method. Ex-tensive 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 https://github.com/Gang-Qu/HATNet-SPI.