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

arXiv: https://arxiv.org/abs/1807.09388 · HTML: https://mlsanity.com/paper/1807.09388/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

## Built on by (most cited first, 18 of 18)

- [PhotoWCT2: Compact Autoencoder for Photorealistic Style Transfer Resulting from Blockwise Training and Skip Connections of High-Frequency Residuals](https://mlsanity.com/paper/2110.11995/) (2021)
- [DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain](https://mlsanity.com/paper/2207.07340/) (2022)
- [Privacy-Preserving Face Recognition Using Random Frequency Components](https://mlsanity.com/paper/2308.10461/) (2023)
- [CSformer: Bridging Convolution and Transformer for Compressive Sensing](https://mlsanity.com/paper/2112.15299/) (2021)
- [FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform](https://mlsanity.com/paper/2111.10800/) (2021)
- [FSOINet: Feature-Space Optimization-Inspired Network for Image Compressive Sensing](https://mlsanity.com/paper/2204.05503/) (2022)
- [Image Compressed Sensing Using Non-local Neural Network](https://mlsanity.com/paper/2112.03712/) (2021)
- [Global Sensing and Measurements Reuse for Image Compressed Sensing](https://mlsanity.com/paper/2206.11629/) (2022)
- [Learning a self-supervised tone mapping operator via feature contrast masking loss](https://mlsanity.com/paper/2110.09866/) (2021)
- [Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing](https://mlsanity.com/paper/2208.01827/) (2022)
- [Deep Unfolding Network for Image Compressed Sensing by Content-adaptive Gradient Updating and Deformation-invariant Non-local Modeling](https://mlsanity.com/paper/2310.10033/) (2023)
- [Adaptive and Cascaded Compressive Sensing](https://mlsanity.com/paper/2203.10779/) (2022)
- [Deep Network for Image Compressed Sensing Coding Using Local Structural Sampling](https://mlsanity.com/paper/2402.19111/) (2024)
- [Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing](https://mlsanity.com/paper/2503.08429/) (2025)
- [OpenICS: Open Image Compressive Sensing Toolbox and Benchmark](https://mlsanity.com/paper/2103.00652/) (2021)
- [Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning](https://mlsanity.com/paper/2207.06746/) (2022)
- [Hierarchical Interactive Reconstruction Network For Video Compressive Sensing](https://mlsanity.com/paper/2304.07473/) (2023)
- [Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack](https://mlsanity.com/paper/2403.10558/) (2024)

## Abstract (arXiv)

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.