LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction

TL;DR

LAPRAN is a scalable neural network that progressively reconstructs high-quality images from compressive sensing measurements using a Laplacian pyramid structure, enabling flexible resolutions and superior performance.

Contribution

This work introduces LAPRAN, a novel scalable pyramid-based adversarial network for flexible, high-fidelity image reconstruction in compressive sensing, outperforming existing methods.

Findings

Achieves an average 7.47dB PSNR improvement over baselines.

Provides a flexible resolution reconstruction adaptable to various compression ratios.

Outperforms existing methods with significant SSIM improvements.

Abstract

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.