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publications
Incremental and Multi-Task Learning Strategies for Coarse-To-Fine Semantic Segmentation
Published in Technologies (MDPI), 2019
This paper is about novel approaches for incremental and multi-task learning for semantic image segmentation task.
Recommended citation: Mel, Mazen, Umberto Michieli, and Pietro Zanuttigh. "Incremental and multi-task learning strategies for coarse-to-fine semantic segmentation." Technologies 8.1 (2019): 1. https://www.mdpi.com/2227-7080/8/1/1
Deep learning-based hyperspectral image reconstruction from emulated and real computed tomography imaging spectrometer data
Published in Optical Engineering (SPIE), 2022
This paper is a learning based apporach for hyper-spectral image reconstruction using CTIS measurements.
Recommended citation: Zimmermann, Markus, et al. "Deep learning-based hyperspectral image reconstruction from emulated and real computed tomography imaging spectrometer data." Optical Engineering 61.5 (2022): 053103-053103. https://www.spiedigitallibrary.org/journals/optical-engineering/volume-61/issue-5/053103/Deep-learning-based-hyperspectral-image-reconstruction-from-emulated-and-real/10.1117/1.OE.61.5.053103.pdf
Joint Reconstruction and Super Resolution of Hyper-Spectral CTIS Images
Published in The 33rd British Machine Vision Conference (BMVC), 2022
This paper is about hyper-spectral image reconstruction and spatial super-resolution from CTIS measurements.
Recommended citation: Mel, Mazen, Alexander Gatto, and Pietro Zanuttigh. "Joint Reconstruction and Super Resolution of Hyper-Spectral CTIS Images." BMVC. 2022. https://bmvc2022.mpi-inf.mpg.de/1063.pdf
Material characterization using a compact Computed Tomography Imaging Spectrometer with super-resolution capability
Published in Proceedings of the 6th International Conference on Optical Characterization of Materials, 2022
This paper is about hyper-spectral imaging and material characterization using spectral signature of objects.
Recommended citation: Amann, Simon, et al. "Material Characterization using a Compact Computed Tomography Imaging Spectrometer with Super-resolution Capability." Proceedings of the 6th International Conference on Optical Characterization of Materials, OCM 2023. https://www.research.unipd.it/handle/11577/3477950
End-to-end learning for joint depth and image reconstruction from diffracted rotation
Published in The Visual Computer, 2023
This paper is about monocular depth estimation using engineered camera Point Spread Function (PSF).
Recommended citation: Mel, Mazen, Muhammad Siddiqui, and Pietro Zanuttigh. "End-to-end learning for joint depth and image reconstruction from diffracted rotation." The Visual Computer (2023): 1-17. https://link.springer.com/article/10.1007/s00371-023-03147-8
Joint Reconstruction and Spatial Super-resolution of Hyper-Spectral CTIS Images via Multi-Scale Refinement
Published in IEEE Transactions on Computational Imaging, 2024
This paper is about spectral image reconstruction and spatial ruper-resolution from CTIS measurements using multi-scale learning.
Recommended citation: Mel, Mazen, Alexander Gatto, and Pietro Zanuttigh. "Joint Reconstruction and Spatial Super-resolution of Hyper-Spectral CTIS Images via Multi-Scale Refinement." IEEE Transactions on Computational Imaging. https://ieeexplore.ieee.org/document/10640287
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching Assistant: Computer Vision Course
Grad course, University of Padova, Department of Information Engineering, 2022
Assisted in the Computer Vision course offered by the departement of Information Engineering at UNIPD and taught by Prof. Pietro Zanuttigh.
Teaching Assistant: Machine Learning Course
Grad course, University of Padova, Department of Information Engineering, 2022
Assisted in the Machine Learning course offered by the departement of Information Engineering at UNIPD and taught by Prof. Pietro Zanuttigh.