  This paper addresses the single-image compressive sensing (CS) and
reconstruction problem. We propose a scalable Laplacian pyramid reconstructive
adversarial network (LAPRAN) that enables high-fidelity, flexible and fast CS
images reconstruction. LAPRAN progressively reconstructs an image following the
concept of Laplacian pyramid through multiple stages of reconstructive
adversarial networks (RANs). At each pyramid level, CS measurements are fused
with a contextual latent vector to generate a high-frequency image residual.
Consequently, LAPRAN can produce hierarchies of reconstructed images and each
with an incremental resolution and improved quality. The scalable pyramid
structure of LAPRAN enables high-fidelity CS reconstruction with a flexible
resolution that is adaptive to a wide range of compression ratios (CRs), which
is infeasible with existing methods. Experimental results on multiple public
datasets show that LAPRAN offers an average 7.47dB and 5.98dB PSNR, and an
average 57.93% and 33.20% SSIM improvement compared to model-based and
data-driven baselines, respectively.
