  Reconfigurable intelligent surfaces (RISs) have emerged as a promising
auxiliary technology for radio frequency imaging. However, existing works face
challenges of faint and intricate back-scattered waves and the restricted
field-of-view (FoV), both resulting from complex target structures and a
limited number of antennas. The synergistic benefits of multi-RIS-aided imaging
hold promise for addressing these challenges. Here, we propose a dual-RIS-aided
imaging system, Dreamer, which operates collaboratively in complementary modes
(reflection-mode and transmission-mode). Dreamer significantly expands the FoV
and enhances perception by deploying dual-RIS across various spatial and
measurement patterns. Specifically, we perform a fine-grained analysis of how
radio-frequency (RF) signals encode scene information in the scattered object
modeling. Based on this modeling, we design illumination strategies to balance
spatial resolution and observation scale, and implement a prototype system in a
typical indoor environment. Moreover, we design a novel artificial neural
network with a CNN-external-attention mechanism to translate RF signals into
high-resolution images of human contours. Our approach achieves an impressive
SSIM score exceeding 0.83, validating its effectiveness in broadening
perception modes and enhancing imaging capabilities. The code to reproduce our
results is available at https://github.com/fuhaiwang/Dreamer.
