Sampling templates guided compression reconstruction network

Linhan Xu; Jun Ke

doi:10.1117/12.2664166

13 June 2023 Sampling templates guided compression reconstruction network

Linhan Xu, Jun Ke

Proceedings Volume 12523, Computational Imaging VII ; 125230F (2023) https://doi.org/10.1117/12.2664166
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

For reconstruction in spatial compressive imaging, we use a module to fuse and extract the information in sampling templates, this obtained feature vector becomes the attention weight, which is multiplied with the feature maps of the compressed measurement frames. In addition, unlike previous networks using segmented images, we use full measurement frames collected as our network input. Thus the local information of objects can be preserved and blocky effect can be avoid. We have tested the network performance on the datasets, Set5, Set14, BSD100, Urban100, Manga109, with 25% compression rate, respectively. We obtain the PSNR\SIM values in the range, [26.5dB, 31.9dB]\[0.82, 0.90]. This result is better than [23.6dB, 29.0dB]\[0.72, 0.85] obtained using the best algorithms in the same application based on our knowledge.

Citation Download Citation

Linhan Xu and Jun Ke "Sampling templates guided compression reconstruction network", Proc. SPIE 12523, Computational Imaging VII , 125230F (13 June 2023); https://doi.org/10.1117/12.2664166

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