Colloquia and Seminars

In image restoration, traditional supervised methods that seek to restore the source enjoy exceptional distortion performance but lack visual quality. With the emergence of powerful generative algorithms, many approaches focus on image realism and diversity, but forsake faithfulness to the source. Motivated by recent theoretical findings, we present a practical algorithm that optimizes source fidelity while aiming for photo-realistic results. Our method optimally transports the distribution of MMSE estimate to the natural image distribution using a simple patch-level deep representation (as simple as an auto-encoder). The results of our experiments demonstrate that we can effectively improve the perceptual quality of MMSE estimates on severe degradations.

Proper X-ray radiation design (via dynamic fluence field modulation, FFM) allows reducing effective radiation dose in computed tomography without compromising image quality. It takes into account patient anatomy, radiation sensitivity of different organs and tissues, and location of regions of interest. We account for all these factors within a general convex optimization framework. Joint work with Anatoli Juditsky and Arkadi Nemirovski Short bio: Michael Zibulevsky received his BS-MS in Electrical Engineering from MIIT - Moscow Institute of Transportation Engineering, and PhD in Operations Research from the Technion - Israel Institute of Technology. He is currently with the Dept. of Computer Science at the Technion. Michael Zibulevsky is one of the founders of Sparse Component Analysis. His research interests include numerical methods of optimization, sparse signal representations, deep neural networks and their applications in signal/image processing and inverse problems.

Balanced sequences and balanced codes have attracted a lot of research in the last seventy years due to their diverse applications in information theory as well as other areas of computer science and engineering. There have been some methods to classify balanced sequences. This work suggests two new different hierarchies to classify these sequences. The first one is based on the largest $\ell$ for which each $\ell$-tuple is contained the same amount of times in the sequence. This property is a generalization for the property required for de Bruijn sequences. The second hierarchy is based on the number of balanced derivatives of the sequence. Enumeration for each such family of sequences and efficient encoding and decoding algorithms are provided in this work.