CS-Fnet: A Compressive Sampling Frequency Neural Network for Simultaneous Image Compression and Recognition

Abstract

Data recognition using compressive measurements is desired for intelligent edge devices to save computation and communication resources. However, direct recognition of compressed image data is often difficult because the compression operation disturbs the original signal structure. In compressive sensing (CS), original signals are transformed into the frequency domain or other domains for sparse representations. This paper presents a compressive sampling frequency neural network (CS- Fnet) to achieve high computational efficiency for compressed image recognition, whose measurement matrix (MM) is automatically obtained through the CS- Fnet training. Furthermore, the MM is constructed in the form of the Kronecker product, which can reduce the number of MM parameters, and hence the CS-Fnet training can achieve much higher computational efficiency and convergence speed. The proposed method is validated using the MNIST dataset and gesture datasets. The experiment results demonstrate that the proposed CS- Fnet outperforms traditional convolution neural networks (CNNs) in terms of image recognition accuracy, and the learned MMs yield higher reconstruction accuracy than traditional MMs.