LAPRAN: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction

Kai Xu; Zhikang Zhang; Fengbo Ren

doi:10.1007/978-3-030-01249-6_30

LAPRAN: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction

Kai Xu, Zhikang Zhang, Fengbo Ren

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Scopus citations

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 the 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 7.47 dB and 5.98 dB PSNR, and an average 57.93 % and 33.20 % SSIM improvement compared to model-based and data-driven baselines, respectively.

Original language	English (US)
Title of host publication	Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
Editors	Martial Hebert, Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss
Publisher	Springer Verlag
Pages	491-507
Number of pages	17
ISBN (Print)	9783030012489
DOIs	https://doi.org/10.1007/978-3-030-01249-6_30
State	Published - 2018
Event	15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: Sep 8 2018 → Sep 14 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11214 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th European Conference on Computer Vision, ECCV 2018
Country/Territory	Germany
City	Munich
Period	9/8/18 → 9/14/18

Keywords

Compressive sensing
Feature fusion
Laplacian pyramid
Reconstruction
Reconstructive adversarial network

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-01249-6_30

Cite this

Xu, K., Zhang, Z., & Ren, F. (2018). LAPRAN: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction. In M. Hebert, V. Ferrari, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings (pp. 491-507). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11214 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01249-6_30

LAPRAN: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction. / Xu, Kai; Zhang, Zhikang; Ren, Fengbo.
Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. ed. / Martial Hebert; Vittorio Ferrari; Cristian Sminchisescu; Yair Weiss. Springer Verlag, 2018. p. 491-507 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11214 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xu, K, Zhang, Z & Ren, F 2018, LAPRAN: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction. in M Hebert, V Ferrari, C Sminchisescu & Y Weiss (eds), Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11214 LNCS, Springer Verlag, pp. 491-507, 15th European Conference on Computer Vision, ECCV 2018, Munich, Germany, 9/8/18. https://doi.org/10.1007/978-3-030-01249-6_30

Xu K, Zhang Z, Ren F. LAPRAN: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction. In Hebert M, Ferrari V, Sminchisescu C, Weiss Y, editors, Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. Springer Verlag. 2018. p. 491-507. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01249-6_30

Xu, Kai ; Zhang, Zhikang ; Ren, Fengbo. / LAPRAN : A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction. Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings. editor / Martial Hebert ; Vittorio Ferrari ; Cristian Sminchisescu ; Yair Weiss. Springer Verlag, 2018. pp. 491-507 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - 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 the 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 7.47 dB and 5.98 dB PSNR, and an average 57.93 % and 33.20 % SSIM improvement compared to model-based and data-driven baselines, respectively.

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LAPRAN: A scalable laplacian pyramid reconstructive adversarial network for flexible compressive sensing reconstruction

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