research-article

G2-DUN: Gradient Guided Deep Unfolding Network for Image Compressive Sensing

Authors:

Debin ZhaoAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 7933 - 7942

https://doi.org/10.1145/3581783.3612339

Published: 27 October 2023 Publication History

Abstract

Inspired by certain optimization solvers, the deep unfolding network (DUN) usually inherits a multi-phase structure for image compressive sensing (CS). However, in existing DUNs, the message transmission within and between phases still faces two issues: 1) the roughness of transmitted information, e.g., the low-dimensional representations. 2) the inefficiency of transmitted policy, e.g., simply concatenating deep features. In this paper, by unfolding the Proximal Gradient Descent (PGD) algorithm, a novel gradient guided DUN (G2 -DUN) for image CS is proposed, in which a gradient map is delicately introduced within each phase for providing richer informational guidance at both intra-phase and inter-phase levels. Specifically, corresponding to the gradient descent (GD) of PGD, a gradient guided GD module is designed, in which the gradient map can adaptively guide step size allocation for different textures of input image, realizing a content-aware gradient updating. On the other hand, corresponding to the proximal mapping (PM) of PGD, a gradient guided PM module is developed, in which the gradient map can dynamically guide the exploring of deep textural priors in multi-scale space, achieving the dynamic perception of the proposed deep model. By introducing the gradient map, the proposed message transmission system not only facilitates the informational communication between different functional modules within each phase, but also strengthens the inferential cooperation among cascaded phases. Extensive experiments manifest that the proposed G2 -DUN outperforms existing state-of-the-art CS methods.

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Cited By

Han KWang JShi YLing NYin BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)D3U-Net: Dual-Domain Collaborative Optimization Deep Unfolding Network for Image Compressive SensingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681532(9952-9960)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681532
Wang XGan H(2024)UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02376(25149-25159)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02376

Index Terms

G2-DUN: Gradient Guided Deep Unfolding Network for Image Compressive Sensing
1. Computing methodologies
  1. Modeling and simulation

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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MM '23: The 31st ACM International Conference on Multimedia

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Cited By

Han KWang JShi YLing NYin BCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)D3U-Net: Dual-Domain Collaborative Optimization Deep Unfolding Network for Image Compressive SensingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681532(9952-9960)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681532
Wang XGan H(2024)UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02376(25149-25159)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02376

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