Big Data &amp;#x2018;Fork&amp;#x2019;: Tensor Product for DCT-II/DST-II/ DFT/HWT

Lee, Moon Ho; Khan, Md. Hashem Ali

doi:10.1109/bmsb.2014.6873510

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…1. an input layer with P nodes; 2. a flatten layer; 3. a DCT transform layer with P nodes using fixed parameters, that is, Kronecker product form of sparse transform matrix Q = R ⊗ R A applied to block vectors, where R is the discrete cosine transform (DCT) matrix applied to block matrices [12]; 4. a trainable compressed sensing layer with P O nodes, O ≪ 1;…”

Section: B Class-specific Neural Network For Block Compressed Sensingmentioning

confidence: 99%

Class-Specific Neural Network for Video Compressed Sensing

Pei

Liu

Ling

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Compressed sensing is an effective solution for signal acquisition and signal reconstruction at a much lower rate than the Nyquist rate. Traditional methods, such as orthogonal matching pursuit and basis pursuit, for image compressed sensing reconstruction have unsatisfying reconstruction quality and long reconstruction time. Researchers now focus on neural network and deep learning methods for the better reconstruction of compressed-sensed signals at a very low sampling rate and a fast speed. However, current neural network approaches for image compressed sensing do not consider the similarities between images or within images, or the types of image blocks; thus, performing poorly in images with complex contents. In this paper, we develop a novel neural network framework that utilizes the similarities between image blocks through Gaussian-mixture models without recording the similarity information to achieve better reconstruction quality than the state-of-the-art neural network methods for block-level image compressed sensing.

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Section: B Class-specific Neural Network For Block Compressed Sensingmentioning

confidence: 99%