Tuesday, June 11, 2019

Deep Learning based compressive sensing - Highly Technical Reference Page/Aggregator, implementation -

** Nuit Blanche is now on Twitter: @NuitBlog **

Thuong Nguyen Canh just sent me the following e-mail featuring a new Highly Technical Reference Page/Aggregator on Deep Learning based compressive sensing as well as two of his recent papers with their implementations.
Dear Igor,

I just want to share a Github repo that I am maintaining for Deep Learning based compressive sensing and our recent paper toward multi-scale deep compressive sensing.

1. Multi-Scale Deep Compressive Sensing Network, IEEE VCIP 2018.  
Abstract: With joint learning of the sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at low subrates. It is understood due to relatively much low-frequency information captured into the sampling matrix. This behavior happens similarly in the multi-scale sampling scheme which also samples more low-frequency components. This paper proposes a multi-scale DCS (MS-DCSNet) based on convolutional neural network. Firstly, we convert image signal using multiple scale-based wavelet transform. Then, the signal is captured through the convolution block by block across scales. The initial reconstructed image is directly recovered from multi-scale measurements. Multi-scale wavelet convolution is utilized to enhance the final reconstruction quality. The network learns to perform both multi-scale in sampling and reconstruction thus results in better reconstruction quality.

Source Code

2. Difference of Convolution for Deep Compressive Sensing, IEEE ICIP 2019 
Deep learning-based compressive sensing (DCS) has improved the compressive sensing (CS) with fast and high reconstruction quality. Researchers have further extended it to multi-scale DCS which improves reconstruction quality based on Wavelet decomposition. In this work, we mimic the Difference of Gaussian via convolution and propose a scheme named as Difference of convolution-based multi-scale DCS (DoC-DCS). Unlike the multi-scale DCS based on a well-designed filter in the wavelet domain, the proposed DoC-DCS learns decomposition, thereby, outperforms other state-of-the-art compressive sensing methods.

Source code

Best regards,
Thuong Nguyen Canh

Thanks Thuong !

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