B5534 - COMPUTATIONAL IMAGING

Learning outcomes

At the end of the course the student knows about computational imaging methods and applications with a focus on solving inverse problems in imaging, such as denoising, deconvolution, single-pixel imaging, and others. He can solve some of the previous imaging problems by using both classic optimization algorithms and modern data-driven approaches with convolutional neural networks (CNNs).

Course contents

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Basic concepts of image formation and noise modeling

– Mathematical tools for image processing: filters, discrete Fourier transform

– Computational imaging applications as inverse problems: denoising, deblurring, super-resolution, segmentation, tomographic reconstruction, …

– Classical methods based on regularization for solving computational imaging problems

– Convolutional neural network approaches: study of architectures and state-of-the-art imaging losses

– Generative approaches: Generative Adversarial Networks (GANs), Diffusion Models, and their applications in computational imaging. Recent developments

– Hands-on sessions using Python and PyTorch

Readings/Bibliography

Notes and slides of the teachers 

Teaching methods

Frontal lessons and exercises with one's own laptop

Assessment methods

Delivery and discussion of a project assigned by the teacher at the end of the course. The project will be discussed by means of slides.

Teaching tools

Slides and codes given by the teacher.

Office hours

See the website of Elena Loli Piccolomini

See the website of Davide Evangelista