Traitement d'images : analyse fréquentielle et multi-échelle from Coursera

Ce MOOC vous propose de découvrir des notions avancées sur l'analyse fréquentielle et multi-échelle. Il aborde les notions de fréquences spatiales, de filtrage en fréquence, les limites de la transformée de Fourier et ouvre la discussion sur la transformée en ondelettes.

Ce que vous allez apprendre :

• Découvrir et appliquer la transformée de Fourier à 2 dimensions et le filtrage dans le domaine fréquentiel

• Comprendre les principes de la déconvolution

• Appliquer la représentation en fréquence au cas de la compression jpeg

• Définir une représentation multi-échelle

Pour suivre ce cours, des bases du langage de programmation Python sont nécessaires : boucles, opérateur logique, vectorisation des opérations , définition de fonction, tableaux et numpy. Vous devez également maitriser les bases du traitement des images (filtrages linéaires par convolution, notion d'histogramme) ainsi que leur manipulation grâce au langage Python (chargement, affichage).

Une attestation de suivi avec succès est attribuée par Coursera aux apprenants réussissant à obtenir une note supérieure à 50 %.

Ce cours a été créé avec le soutien de la Fondation Patrick & Lina Drahi.

What's inside

Syllabus

Transformée de Fourier et filtrage séquentielle

Déconvolution

Transformée en cosinus discrète et compression JPEG

Transformée en ondelettes et décomposition multiéchelle

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Adaptation aux besoins des professionnels du traitement d'images afin de se perfectionner sur des notions avancées d'analyse fréquentielle et multi-échelle

Approfondissement des concepts fondamentaux de l'analyse fréquentielle, notamment la transformée de Fourier et la transformée en ondelettes

Exploration de méthodes de pointe telles que la déconvolution et la compression JPEG, largement utilisées dans le domaine de l'imagerie

Career center

Learners who complete Traitement d'images : analyse fréquentielle et multi-échelle will develop knowledge and skills that may be useful to these careers:

Professor, Image Processing

For Professors specializing in Image Processing, it is imperative to stay abreast of the latest developments in the field. This course will provide Professors with an updated and comprehensive understanding of frequency-domain image processing, enabling them to effectively teach and guide students.

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Research Scientist, Image Processing

Research Scientists in Image Processing rely on advanced image processing techniques to push the boundaries of the field. This course offers an in-depth exploration of frequency-domain image processing, empowering Research Scientists to conduct cutting-edge research.

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Computational Photographer

Computational Photography blends image processing with computer science to create innovative imaging techniques. This course will equip Computational Photographers with a solid understanding of frequency-domain image processing.

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Computer Vision Engineer

Image analysis is at the core of a Computer Vision Engineer's work, and the Transformée de Fourier is widely used in the field. By enrolling in this course, a Computer Vision Engineer will gain a deeper and more nuanced understanding of the Fourier Transform.

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Software Engineer, Image Processing

Software Engineers specializing in Image Processing require a deep understanding of image processing algorithms. This course will provide them with the foundational knowledge of frequency-domain image processing, enabling them to develop innovative solutions.

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Medical Imaging Scientist

For a Medical Imaging Scientist, a comprehensive understanding of image processing techniques is essential. This course imparts valuable knowledge about the Transformée de Fourier and the Transformée en ondelettes, expanding the toolkit of a Medical Imaging Scientist.

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Signal Processing Engineer

A Signal Processing Engineer requires a firm grasp of frequency-based signal analysis techniques. This course offers an in-depth exploration of the Fourier Transform, a critical tool for the analysis of signals in various domains.

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Geophysicist

A Geophysicist relies on image processing to analyze data gathered from various sources. The Fourier Transform is a powerful tool in this field, and this course will provide Geophysicists with the necessary foundation.

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Graphics Programmer

Graphics Programmers utilize image processing techniques to create realistic and visually appealing graphics. This course will provide Graphics Programmers with a theoretical and practical understanding of frequency-domain image processing.

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Machine Learning Engineer

A Machine Learning Engineer often leverages Fourier analysis for feature extraction and dimensionality reduction. This course provides essential knowledge of the Fourier Transform and its applications within the realm of machine learning.

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Biomedical Engineer

Biomedical Engineers leverage image processing techniques for medical diagnosis and research. This course will help Biomedical Engineers build a foundation in frequency-domain image processing, enabling them to make advancements in the field.

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Image Processing Engineer

An Image Processing Engineer may use the Transformée multi-échelle to manipulate and analyze complex images. This course teaches the principles and applications of this transformative technique, making it a valuable resource for those pursuing a career in this field.

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Robotics Engineer

Image processing is becoming increasingly important in Robotics, especially for tasks like object recognition and navigation. This course will equip Robotics Engineers with the fundamental concepts of frequency-domain image processing.

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Astronomer

In the field of Astronomy, image processing is crucial for analyzing celestial data. The Fourier Transform is a vital tool for astronomers, and this course offers a comprehensive introduction to its principles.

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Data Scientist

The Transformée en ondelettes is a foundational and versatile tool for Data Scientists. This course will guide learners through the application of this transformative technique to the field of data science.

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Traitement d'images

analyse fréquentielle et multi-échelle

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