Talks

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General/Introductory

Atomic Decomposition of Images: Advanced Methods in Image Processing
June 6th, 2016
Technion, Israel

Image Processing is a fascinating scientific field, offering ways to handle visual data by computers. How can an image be brought to be stored and processed by a computer? What kind of such processing could be done which are worthwhile? In the first part of this talk we shall describe the core ideas behind the field of image processing by answering these two questions. In the second part of the talk we shall turn to describe the recent research activity in Elad’s group in the Computer-Science department at the Technion, emphasizing the vast work done on harnessing sparse and redundant representation modeling to image processing needs.

This was given as an invited talk (in Hebrew) in the Technion's course "Scientific Discoveries" (114010), on June 6th, 2016. The same talk was also given to Computer-Science undergraduate students on June 8th, as part of the CSta-Cafe students' enrichment activity.
Sparse & Redundant Representation Modeling of Images: Theory and Applications
April 19-24, 2015
ICASSP 2015, "School-of-ICASSP" Session, in Brisbane Australia

In this survey talk I will walk you through a decade of fascinating research activity on “sparse and redundant representations”. We will start with a classic image processing task of noise removal and use it as a platform for the introduction of data models in general, and sparsity and redundancy as specific forces in such models. The emerging model will be shown to lead to a series of key theoretical and numerical questions, which we will handle next. A key problem with the use of sparse and redundant representation modeling is the need for a sparsifying dictionary – we will discuss ways to obtain such a dictionary by learning from examples, and introduce the K-SVD algorithm. Then we will show how all these merge into a coherent theory that can be deployed successfully to various image processing applications.

This talk was given as an invited talk in ICASSP 2015
Sparse Modeling of Graph-Structured Data ... and ... Images
March 13 - 15, 2014
The Institute of Statistical Mathematics, Tachikawa, Tokyo

Images, video, audio, text documents, financial data, medical information, traffic info – all these and many others are data sources that can be effectively processed. Why? Is it obvious? In this talk we will start by discussing “modeling” of data as a way to enable their actual processing, putting emphasis on sparsity-based models. We will turn our attention to graph-structured data and propose a tailored sparsifying transform for its dimensionality reduction and subsequent processing. We shall conclude by showing how this new transform becomes relevant and powerful in revisiting … classical image processing tasks..

This is a joint work with Idan Ram and Israel Cohen. This talk was given as a plenary talk in a Workshop on Mathematical Approaches to Large-Dimensional Data Analysis
Sparse Modeling of Graph-Structured Data ... and ... Images
May 28-29, 2013
Technion, Israel

Images, video, audio, text documents, financial data, medical information, traffic info — all these and many others are data sources that can be effectively processed. Why? Is it obvious? In this talk we will start by discussing “modeling” of data as a way to enable their actual processing, putting emphasis on sparsity-based models. We will turn our attention to graph-structured data and propose a tailored sparsifying transform for its dimensionality reduction and subsequent processing. We shall conclude by showing how this new transform becomes relevant and powerful in revisiting … classical image processing tasks.

This is a joint work with Idan Ram and Israel Cohen. This talk was given as an invited talk in the 3rd Annual International TCE Conference on Machine Learning & Big Data, May 28-29, 2013, in the Technion, Israel.
Sparse & Redundant Representation Modeling of Images: Theory and Applications
April, 2012
EE-Seminar in Tel-Aviv University

In this survey talk I will walk you through a decade of fascinating research activity on “sparse and redundant representations”. We will start with a classic image processing task of noise removal and use it as a platform for the introduction of data models in general, and sparsity and redundancy as specific forces in such models. The emerging model will be shown to lead to a series of key theoretical and numerical questions, which we will handle next. A key problem with the use of sparse and redundant representation modeling is the need for a sparsifying dictionary — we will discuss ways to obtain such a dictionary by learning from examples, and introduce the K-SVD algorithm. Then we will show how all these merge into a coherent theory that can be deployed successfully to various image processing applications.

This talk was given in the EE-Seminar in Tel-Aviv University (April 2012), and also in the Weizmann Institute (May 2012).
Sparse & Redundant Representation Modeling of Images: Theory and Applications
April, 2012
EE-Seminar in Tel-Aviv University

In this survey talk I will walk you through a decade of fascinating research activity on “sparse and redundant representations”. We will start with a classic image processing task of noise removal and use it as a platform for the introduction of data models in general, and sparsity and redundancy as specific forces in such models. The emerging model will be shown to lead to a series of key theoretical and numerical questions, which we will handle next. A key problem with the use of sparse and redundant representation modeling is the need for a sparsifying dictionary — we will discuss ways to obtain such a dictionary by learning from examples, and introduce the K-SVD algorithm. Then we will show how all these merge into a coherent theory that can be deployed successfully to various image processing applications.

This talk was given in the EE-Seminar in Tel-Aviv University (April 2012), and also in the Weizmann Institute (May 2012).