@ARTICLE{Urbaś_Sebastian_Application_2024,
 author={Urbaś, Sebastian and Więcek, Bogusław},
 volume={32},
 number={1},
 journal={Opto-Electronics Review},
 pages={e148877},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences (under the auspices of the Committee on Electronics and Telecommunication) and Association of Polish Electrical Engineers in cooperation with Military University of Technology},
 abstract={The article presents the simulation results of a single-pixel infrared camera image reconstruction obtained by using a convolutional neural network (CNN). Simulations were carried out for infrared images with a resolution of 80 × 80 pixels, generated by a low-cost, low-resolution thermal imaging camera. The study compares the reconstruction results using the CNN and the ℓ1 reconstruction algorithm. The results obtained using the neural network confirm a better quality of the reconstructed images with the same compression rate expressed by the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).},
 type={Article},
 title={Application of a deep-learning neural network for image reconstruction from a single-pixel infrared camera},
 URL={http://www.czasopisma.pan.pl/Content/130027/PDF-MASTER/OPELRE_2024_32_1_S_Urbas.pdf},
 doi={10.24425/opelre.2024.148877},
 keywords={single-pixel imaging, compressive sensing, thermal imaging, convolutional neural network, dataset augmentation},
}