TY - JOUR

T1 - Optimization-inspired Cumulative Transmission Network for image compressive sensing

AU - Zhang, Tianfang

AU - Li, Lei

AU - Peng, Zhenming

N1 - Publisher Copyright: © 2023 Elsevier B.V.

PY - 2023

Y1 - 2023

N2 - Compressive Sensing (CS) techniques enable accurate signal reconstruction with few measurements. Deep Unfolding Networks (DUNs) have recently been shown to increase the efficiency of CS by emulating iterative CS optimization procedures by neural networks. However, most of these DUNs suffer from redundant update procedures or complex matrix operations, which can impair their reconstruction performances. Here we propose the optimization-inspired Cumulative Transmission Network (CT-Net), a DUN approach for natural image CS. We formulate an optimization procedure introducing an auxiliary variable similar to Half Quadratic Splitting (HQS). Unfolding this procedure defines the basic structure of our neural architecture, which is then further refined. A CT-Net is composed of Reconstruction Fidelity Modules (RFMs) for minimizing the reconstruction error and Constraint Gradient Approximation (CGA) modules for approximating (the gradient of) sparsity constraints instead of relying on an analytic solutions such as soft-thresholding. Furthermore, a lightweight Cumulative Transmission (CT) between CGAs in each reconstruction stage is proposed to facilitate a better feature representation. Experiments on several widely used natural image benchmarks illustrate the effectiveness of CT-Net with significant performance improvements and fewer network parameters compared to existing state-of-the-art methods. The experiments also demonstrate the scene and noise robustness of the proposed method.

AB - Compressive Sensing (CS) techniques enable accurate signal reconstruction with few measurements. Deep Unfolding Networks (DUNs) have recently been shown to increase the efficiency of CS by emulating iterative CS optimization procedures by neural networks. However, most of these DUNs suffer from redundant update procedures or complex matrix operations, which can impair their reconstruction performances. Here we propose the optimization-inspired Cumulative Transmission Network (CT-Net), a DUN approach for natural image CS. We formulate an optimization procedure introducing an auxiliary variable similar to Half Quadratic Splitting (HQS). Unfolding this procedure defines the basic structure of our neural architecture, which is then further refined. A CT-Net is composed of Reconstruction Fidelity Modules (RFMs) for minimizing the reconstruction error and Constraint Gradient Approximation (CGA) modules for approximating (the gradient of) sparsity constraints instead of relying on an analytic solutions such as soft-thresholding. Furthermore, a lightweight Cumulative Transmission (CT) between CGAs in each reconstruction stage is proposed to facilitate a better feature representation. Experiments on several widely used natural image benchmarks illustrate the effectiveness of CT-Net with significant performance improvements and fewer network parameters compared to existing state-of-the-art methods. The experiments also demonstrate the scene and noise robustness of the proposed method.

KW - Compressive sensing

KW - Deep unfolding

KW - Image reconstruction

KW - Neural networks

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=85171132561&partnerID=8YFLogxK

U2 - 10.1016/j.knosys.2023.110963

DO - 10.1016/j.knosys.2023.110963

M3 - Journal article

AN - SCOPUS:85171132561

VL - 279

JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

SN - 0950-7051

M1 - 110963

ER -