A CNN reconstruction scheme with low and high frequency branches for image compressive sensing

Liangjun Wang; Chenhao Yang

doi:10.1117/12.2684649

21 July 2023 A CNN reconstruction scheme with low and high frequency branches for image compressive sensing

Liangjun Wang, Chenhao Yang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127172Y (2023) https://doi.org/10.1117/12.2684649
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Recently, Convolutional Neural Network-based Compressive Sensing (CNN-CS) have drawn attention of extensive researchers since it can achieve faster reconstruction speed than traditional ones. Instead of traditional iterative reconstruction algorithms, a CNN reconstruction network is used with the goal of extracting key features of the images. For this purpose, the network should be trained on large amount of image data. However, most images usually poses both abundant low-frequency components and sparse high-frequency components, thus it is unreasonable to extract image features through a single network as in most CNN-CS algorithms due to that they may degrade the feature extraction of each other. In order to get a better result, a two-branch CNN-CS is proposed in the paper. In the network, the image signals are decomposed into two parts according to frequency firstly, and one branch is dedicated to extract the features of low frequency components, while the other is used to learn the high frequency ones. Considering the sparsity of high frequency, Mean Absolute Error (MAE) is chosen as the training penalty loss function. As such, both low and high frequency components can be well reconstructed respectively. Experimental results show that our method is prior to all traditional CS methods and single network CNN-CS method (CSNet+). The gain of PSNR over CSNet+ can reach 0.45dB.

Citation Download Citation

Liangjun Wang and Chenhao Yang "A CNN reconstruction scheme with low and high frequency branches for image compressive sensing", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127172Y (21 July 2023); https://doi.org/10.1117/12.2684649

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KEYWORDS

Image restoration

Education and training

Reconstruction algorithms

Compressed sensing

Cameras

Feature extraction

Image quality

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