High-fidelity single-pixel imaging using multihead attention mechanism

Single-pixel imaging has gained prominence for its wide working wavelength and high sensitivity. Deep learning-based single-pixel imaging shows superiority in real-time reconstruction, particularly with limited resources. In this work, we report a novel encoder-decoder method for single-pixel imaging, which aims at enhancing imaging quality from extremely low measurement amounts. First, we encode the high-dimensional target information into one-dimensional measurements using globally optimized modulation patterns, implemented by a fully connected or convolutional layer. Second, we integrate a U-Net neural network with an advanced multi-head self-attention mechanism and a pyramid pooling module to decode the measurements and reconstruct high-fidelity images. Under such a strategy, the skip connections within the U-Net structure enhance the preservation of fine image features, and the incorporation of the multi-head self-attention mechanism and pyramid pooling module effectively captures contextual dependencies among low-dimensional measurements, thereby extracting significant image features and enhancing reconstruction quality. The simulation results conducted on the STL-10 dataset validate the efficiency of the reported technique. With a resolution of 96 × 96 pixels and an ultra-low sampling rate of 1%, we consistently achieved the highest image fidelity compared to traditional single-pixel reconstruction methods for both grayscale and color images.