AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed Sensing

Li, Nanyu; Zhou, Chunlin

doi:10.1109/icct50939.2020.9295956

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…Meanwhile, it is found that only a few methods can achieve a relatively large receptive field while reconstructing. During upsampling, other methods [ 4 , 23 , 26 , 31 , 35 ] only focus on the corresponding measurements compressed from the target single block while reconstructing. Under normal conditions, a larger receptive field tends to bring better performance.…”

Section: Methodsmentioning

confidence: 99%

IEF-CSNET: Information Enhancement and Fusion Network for Compressed Sensing Reconstruction

Zhou

Liu

Shen

2023

Sensors

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The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm.

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Section: Methodsmentioning

confidence: 99%

IEF-CSNET: Information Enhancement and Fusion Network for Compressed Sensing Reconstruction

Zhou

Liu

Shen

2023

Sensors

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“…First, it is difficult to train the huge end-to-end DNNs without auxiliary structures, such as [18]. In this way, the heavy full connection between any two successive layers [27] and the auxiliary structures to strengthen nonlinear operations [4,18,30,33] greatly affects the utilization efficiency of the GPU devices. Second, some methods cut the image into blocks for processing [27] or reshape the upsampled matrix, which spends time on matrix splicing and the subsequent deblocking filtering.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

EiCSNet: Efficient Iterative Neural Network for Compressed Sensing Reconstruction

et al. 2022

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The rapid growth of sensing data demands compressed sensing (CS) in order to achieve high-density storage and fast data transmission. Deep neural networks (DNNs) have been under intensive development for the reconstruction of high-quality images from compressed data. However, the complicated auxiliary structures of DNN models in pursuit of better recovery performance lead to low computational efficiency and long reconstruction times. Furthermore, it is difficult for conventional neural network designs to reconstruct extra-high-frequency information at a very low sampling rate. In this work, we propose an efficient iterative neural network for CS reconstruction (EiCSNet). An efficient gradient extraction module is designed to replace the complex auxiliary structures in order to train the DNNs more efficiently. An iterative enhancement network is applied to make full use of the limited information available in CS for better iterative recovery. In addition, a frequency-aware weighted loss is further proposed for better image restoration quality. Our proposed compact model, EiCSNet2*1, improved the performance slightly and was nearly seven times faster than its counterparts, which shows that it has a highly efficient network design. Additionally, our complete model, EiCSNet6*1, achieved the best effect at this stage, where the average PSNR was improved by 0.37 dB for all testing sets and sampling rates.

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“…However, to conserve memory, BCS is commonly employed. In approaches such as [14,26,30,[42][43][44], the image is divided into smaller blocks, typically with a size of 33 × 33. These blocks are then vectorized and measured individually.…”

Section: Andmentioning

confidence: 99%

MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)

Yu,

Dansereau

2024

IET Signal Processing

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Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images using CS. We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. To enhance learning ability and incorporate structural diversity, we integrate aggregated residual transformations (ResNeXt) and squeeze-and-excitation mechanisms into the ISTA block. This block serves as a deep equilibrium layer connected to a semi-tensor product network for convenient sampling and providing an initial reconstruction. The resulting model, called MsDC-DEQ-Net, exhibits competitive performance compared to state-of-the-art network-based methods. It significantly reduces storage requirements compared to deep unrolling methods, using only one iteration block instead of multiple iterations. Unlike deep unrolling models, MsDC-DEQ-Net can be iteratively used, gradually improving reconstruction accuracy while considering computation tradeoffs. Additionally, the model benefits from multiscale dilated convolutions, further enhancing performance.

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AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed Sensing

Cited by 8 publications

References 24 publications

IEF-CSNET: Information Enhancement and Fusion Network for Compressed Sensing Reconstruction

IEF-CSNET: Information Enhancement and Fusion Network for Compressed Sensing Reconstruction

EiCSNet: Efficient Iterative Neural Network for Compressed Sensing Reconstruction

MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multiscale Dilated Convolution for Image Compressive Sensing (CS)

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