@misc{Wu_Hao_Optica_2024,
 author={Wu, Hao and Wu, Hui and Su, Xinyu and Wu, Jingjun and Liu, Shuangli},
 contributor={Urbańczyk, Wacław. Redakcja},
 identifier={DOI: 10.37190/oa240308},
 year={2024},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Oficyna Wydawnicza Politechniki Wrocławskiej},
 description={Optica Applicata, Vol. 54, 2024, nr 3, s. 383-394},
 description={Optica Applicata is an international journal, published in a non-periodical form in the years 1971-1973 and quarterly since 1973. From the beginning of the year 2008, Optica Applicata is an Open Access journal available online via the Internet, with free access to the full text of articles serving the best interests of the scientific community. The journal is abstracted and indexed in: Chemical Abstracts, Compendex, Current Contents, Inspec, Referativnyj Zhurnal, SCI Expanded, Scopus, Ulrich’s Periodicals Directory},
 description={http://opticaapplicata.pwr.edu.pl/},
 language={eng},
 abstract={Miniaturized computational spectrometers have become a new research hotspot due to their portability and miniaturization. However, there are several issues, like low precision and poor stability. Because the problem of spectrum reconstruction accuracy is very evident, we suggested a novel approach to raise the reconstruction accuracy. A library of optical filtering functions was acquired using the time-domain finite-difference (FDTD) method. A cross-correlation algorithm was then used to choose 100 sparse filter functions, which were then built as an encoding matrix and then, based on the encoding matrix, a self-attention mechanism algorithm to improve the accuracy. The reconstructed spectrum’s mean square error (MSE) is 0.0019, and its similarity coefficient (R2) is 0.9780. This self-attention mechanism spectral reconstruction technique will open up new possibilities for high-accuracy reconstruction for various computational spectrometer types.},
 type={artykuł},
 title={Optica Applicata, Vol. 54, 2024, nr 3Reconstructing computational spectra using deep learning’s self-attention method},
 keywords={optyka, spectral reconstruction, self-attention, encoding matrix, cross-correlation},
}