Education
2022 - now  : Ph.D. in Computer Science, KAUST, Saudi Arabia.
2020 - 2022: M.Sc. in Computer Science, KAUST, Saudi Arabia.
2015 - 2020: B.Sc. in Electrical Engeneering, UFRN, Brazil.
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Research
My research is centered on the innovative fusion of deep reconstruction algorithms and end-to-end optics design. This involves developing deep learning models and formulating optimization strategies that incorporate optical elements into the computational loop.
Focusing on the following topics:
Minimalistic cameras design.
Deep learning for optical design.
Computational cameras.
Optics-aware computational photography.
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Latent Space Imaging
Matheus Souza, Yidan Zheng, Kaizhang Kang, Yogeshwar Nath Mishra, Qiang Fu, Wolfgang Heidrich
CVPR 2025. Arxiv pre-print (To be updated soon)
New paradigm for very low bandwidth image capture based on generative models latent space.
The demonstration of a range of downstream applications on this latent space with real hardware experimentation.
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Limitations of Data-Driven Spectral Reconstruction - An Optics-Aware Analysis
Qiang Fu*, Matheus Souza*, Suhyun Shin, Eunsue Choi, Seung-Hwan Baek, Wolfgang Heidrich
Computational Optical Sensing and Imaging, 2024. Oral Presentation
Full Paper Under Review. Arxiv pre-print
Comprehensive analysis of state-of-the-art data-driven hyperspectral imaging atypical overfitting.
Optical aberrations can provide encoding power if modeled correctly.
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End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model
Xinge Yang, Matheus Souza, Kunyi Wang, Praneeth Chakravarthula, Qiang Fu, Wolfgang Heidrich
Siggraph Asia 2024. Paper (Arxiv) / Paper (PDF) / Supp (PDF)
Differentiable ray-tracing and wave-propagation model.
End-to-End hybrid refractive-diffractive lenses design with prototypes.
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MetaISP - Exploiting Global Scene Structure for Accurate Multi-Device Color Rendition
Matheus Souza, Wolfgang Heidrich
MetaISP VMV 2023 / Code
CRISPnet: Color rendition ISP net. Paper (Arxiv)
We developed a model for learning multiple commercial ISPs.
Integrating global scene semantics, metadata information, and advanced deep learning techniques.
Collected synthetic and real-world datasets, consisting of RAW-RGB pairs from various devices.
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