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Computational Imaging and Vision

Final assessment for Computational Imaging and Vision course within the PhD program in Information Engineering of the Department of Information Engineering @ University of Pisa, A.A. 2022/2023

The work builds upon the implementation presented in the article titled "Denoising Autoencoders (DAE) — How To Use Neural Networks to Clean Up Your Data" [1].

References

[1] S. Dobilas, "Denoising Autoencoders (DAE) — How To Use Neural Networks to Clean Up Your Data", 2022. [Online]. Available: https://towardsdatascience.com/denoising-autoencoders-dae-how-to-use-neural-networks-to-clean-up-your-data-cd9c19bc6915. [Accessed: 24-Apr-2023]
[2] Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu, and the scikit-image contributors. scikit-image: Image processing in Python. PeerJ 2:e453 (2014) https://doi.org/10.7717/peerj.453
[3] Bihan Wen (2020) reproducible-image-denoising-state-of-the-art [Source code]. https://github.com/wenbihan/reproducible-image-denoising-state-of-the-art#commonly-used-image-quality-metrics
[4] L. Deng, "The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]," in IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 141-142, Nov. 2012, doi: 10.1109/MSP.2012.2211477
[5] S. G. Chang, Bin Yu and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," in IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532-1546, Sept. 2000, doi: 10.1109/83.862633
[6] A. Buades, B. Coll and J. . -M. Morel, "A non-local algorithm for image denoising," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 2005, pp. 60-65 vol. 2, doi: 10.1109/CVPR.2005.38
[7] C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Bombay, India, 1998, pp. 839-846, doi: 10.1109/ICCV.1998.710815