@misc{Ma_Shibang_Robust_2024,
 author={Ma, Shibang and Qin, Yi and Gong, Qiong and Wang, Hongjuan},
 contributor={Urbańczyk, Wacław. Redakcja},
 identifier={DOI: 10.37190/oa240309},
 year={2024},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Oficyna Wydawnicza Politechniki Wrocławskiej},
 description={Optica Applicata, Vol. 54, 2024, nr 3, s. 395-407},
 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={Noise attack is a potential threat to optical cryptosystems because the contaminated ciphertext always yields degraded decrypted result. What is more, such contamination can hardly be eliminated by traditional methods, as the ciphertext itself is also a noise-like image. In this paper, we propose a deep-learning-based approach to deal with this problem. The contaminated ciphertexts, which produce unrecognized decrypted images, can yield high quality ones after being repaired by a deep neural network. We take the diffractive-imaging-based encryption (DIBE) scheme as an example to illustrate our method. Numerical results are presented to show the feasibility and validity of the proposal.},
 type={artykuł},
 title={Robust encryption in diffractive-imaging-based encryption scheme using deep learning},
 keywords={optyka, robust encryption, noise attack deep learning, diffractive-imaging-based encryption},
}