JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing

• URL: http://arxiv.org/abs/2211.05963v1
• Date: Fri, 11 Nov 2022 02:20:30 GMT
• Title: JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing
• Authors: Chunyan Zeng, Jiaxiang Ye, Zhifeng Wang, Nan Zhao, Minghu Wu
• Abstract summary: Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. This framework outperforms many other state-of-the-art methods, especially at low sampling rates.
• Score: 8.902545322578925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on well-designed cascade neural networks. Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the sampling sub-network, an adaptive full connected layer instead of the traditional random matrix is used to mimic the sampling operator. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. The SDA is used to solve the signal mapping problem and the signals are initially reconstructed. Furthermore, CNN is used to fully recover the structure and texture features of the image to obtain better reconstruction performance. Extensive experiments show that this framework outperforms many other state-of-the-art methods, especially at low sampling rates.

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