IMA 206
Photographie computationelle / Methodes par patchs

Responsables : M. Roux et Y. Tendero - (Telecom ParisTech/LTCI)
  1. Descriptif de l'UE
  2. Modalites de validation
  3. Sujets de projet
  4. Supports de cours (+ syllabus)
  5. Donnees projet


Descriptif de l'UE

IMA 206 sur Eole (acces restreint)



N'oubliez pas de choisir un sujet de projet pour le 24/02/17.


Modalites de validation

L'examen ecrit final aura lieu le 21 avril de 15h15 a 16h45 en C46. La note finale sera la moyenne de la note de l'examen final et d'une note de mini-projet.
La liste les projets de trouve plus bas sur cette page.
Pour l'attribution des projets envoyez un courriel a Y. Tendero (yohann.tendero@telecom-paristech.fr) au plus tard le 24/02/17. Ce courriel mentionnera :
- Trois voeux de sujet;
- Eventuellement le nom d'un eleve avec lequel vous prefereriez travailler.
- Le sujet de votre message devra etre: [Voeux IMA206].
Pour vous aider a formuler vos voeux vous pouvez consulter les references associees et/ou contacter l'encadrant si vous avez des questions.

Sujets de projet

Les projets seront encadres par des enseignants-chercheurs du groupe Images du laboratoire de l'ecole, le LTCI.

  1. Mise en correspondance de graphe par analyse spectrale (M. Roux, F. Tupin)
  2. Similarite entre images medicales (I. Bloch)
  3. Segmentation and Classification of Skin Lesions for Disease Diagnosis (P. Gori)
  4. An automated image denoising chain (Y. Tendero)
  5. Non-uniformity correction by guided filter (Y. Tendero)
  6. Removing camera shake (Y. Tendero)
  7. Haze removal (Y. Tendero)
  8. From flutter shutter imaging to blind uniform motion debluring (Y. Tendero)

Mise en correspondance de graphe par analyse spectrale

Descriptif:

L'objectif de ce projet est d'evaluer une methode de mise en correspondance de graphes. Celle-ci s'appuie sur la definition d'une matrice de similarite et de l'optimisation d'une fonction de cout visant a avoir les appariements entre noeuds des deux graphes les plus coherents possibles. On etudiera en particulier comment la definition des differents termes (similarite et association) influence les resultats obtenus. On etudiera egalement differents cadres applicatifs de cette approche (macthing de SIFT, graphes de composantes connexes, etc.).

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Encadrants:

M. Roux et F. Tupin. Contacts michel.roux@telecom-paristech.fr, florence.tupin@telecom-paristech.fr.

Language de programmation:

au choix (a discuter en debut de projet).

Article:

[1] M. Leordeanu, M. Hebert. "A Spectral Technique for Correspondence Problems Using Pairwise Constraints", ICCV 2005.
http://perso.telecom-paristech.fr/~tupin/TPGRAPHCUT/LEORDEANU/article_LH.pdf



Morphologie mathematique non locale pour le filtrage d'images

Descriptif:

Le but de ce projet est de combiner les proprietes des filtres morphologiques, combinant ouvertures et fermetures, et des filtres non locaux. Les elements structurants utilises pour definir les operations de base de la morphologie mathematique (dilation, erosion, et leurs combinaisons) seront definis de maniere adaptative a partir d'une similarite entre "patchs" (voisinages) centres sur le pixel a traiter et sur d'autres pixels de l'image. Les filtres ainsi construits seront testes sur divers types d'images afin d'illustrer leur capacite a supprimer du bruit. Des etapes de reconstruction pourront etre ajoutees apres chaque operation d'ouverture ou de fermeture, afin de gagner en robustesse et de mieux preserver les contours des objets dans les images.

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Encadrants:

I. Bloch et Y. Gousseau. Contacts isabelle.bloch@telecom-paristech.fr, yann.gousseau@telecom-paristech.fr.

Language de programmation:

A definir.

Article:

[1] P. Salembier. Study on nonlocal morphological operators. 16th IEEE International Conference on Image Processing (ICIP), 2009.
P. Salembier. Study on nonlocal morphological operators. 16th IEEE International Conference on Image Processing (ICIP), 2009



Segmentation and Classification of Skin Lesions for Disease Diagnosis

Descriptif:

L'objectif de ce projet est de mettre en oeuvre une methode de segmentation, extraction de features et classification sur des images des lesions pigmentees de la peau in vivo (dermatoscopie).

Encadrants:

P. Gori. Contact pietro.gori@telecom-paristech.fr,

Language de programmation:

Matlab ou Python.

Article:

[1] Sumithra R et al. "Segmentation and Classification of Skin Lesions for Disease Diagnosis", International Conference on Advanced Computing Technologies and Applications (ICACTA), 2015
https://arxiv.org/pdf/1609.03277v1.pdf



An automated image denoising chain

Descriptif:

In any digital image, the measurement of the three observed color values at each pixel is subject to some perturbations. These perturbations can be (partially) compensated as depicted below.

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These perturbations are due to the random nature of the photon counting process in each sensor and impedes the performance of subsequent processing (detection, 3D reconstruction from stereo pairs, etc.). The method presented in [1] changed the state-of-the-art of denoising methods. Yet, to apply [1] one has to know/estimate the noise variance in the image. This project proposes to use the method presented in [2] to perform this estimation and produce an automated image processing chain.

Encadrant:

Y. Tendero. Contact: yohann.tendero@telecom-paristech.fr.

Language de programmation:

Matlab, Octave, C/C++.

Articles:

[1] A. Buades, B. Coll, and J.-M. Morel. "Non-Local Means Denoising", Image Processing On Line, 1 (2011).
http://www.ipol.im/pub/art/2011/bcm_nlm/
[2] M. Colom, and A. Buades. "Analysis and Extension of the Percentile Method, Estimating a Noise Curve from a Single Image", Image Processing On Line, 3 (2013), pp. 332-359.
http://www.ipol.im/pub/art/2013/45/



Non-uniformity correction by guided filter

Descriptif:

The correction of non-uniformity infrared sensor response is still a challenging task of interest for eg. NASA, DGA. In a nutshell, the observed images are severely corrupted by vertical stripes. An exemple of the observed and corrected data, of the Mars planet are depicted below.

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These stripes are due to non-uniform response of photodetectors. This project proposes to implement the method proposed in a recent paper [1] and to test the method on real raw infrared data of e.g. Mars Orbiter satellite, low cost infrared cameras.

Encadrant:

Y. Tendero. Contact: yohann.tendero@telecom-paristech.fr.

Language de programmation:

Matlab, Octave, C/C++.

Article:

[1] Y. Cao, M.Y. Yang, C.-L. Tisse. "Effective Strip Noise Removal for Low-textured Infrared Images Based on 1D Guided Filtering," in Circuits and Systems for Video Technology, IEEE Transactions on, 2015.
Effective Strip Noise Removal for Low-textured Infrared Images Based on 1D Guided Filtering



Removing camera shake

Descriptif:

Videos captured with hand-held cameras often suffer from a significant amount of blur, mainly caused by the inevitable natural tremor of the photographer's hand. In [1], the Authors propose an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video.

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Encadrant:

Y. Tendero. Contact: yohann.tendero@telecom-paristech.fr.

Language de programmation:

Matlab, Octave, C/C++.

Article:

[1] M. Delbracio, G. Sapiro. "Removing Camera Shake via Weighted Fourier Burst Accumulation", Image Processing, IEEE Transactions on 24 (11), 3293-3307, 2015.
Removing Camera Shake via Weighted Fourier Burst Accumulation



Haze removal

Descriptif:

Haze is an annoying problem for photographers since it degrades image quality. It is also a threat to the reliability of many applications, like outdoor surveillance, object detection, and aerial imaging. So removing haze from images is important in computer vision/graphics. The goal of the project is to implement [1].

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Encadrant:

Y. Tendero. Contact: yohann.tendero@telecom-paristech.fr.

Language de programmation:

Matlab, Octave, C/C++.

Article:

[1] HE, Kaiming. "Single Image Haze Removal Using Dark Channel Prior", Ph.D. thesis, Chapter 3, 2011.
Single Image Haze Removal Using Dark Channel Prior, Ph.D. thesis, Chapter 3, 2011.


From flutter shutter imaging to blind uniform motion debluring

Descriptif:

One of the major problems of photography is the noise/blur dilemma that limitates the image quality when imaging dynamic scenes. If the time-exposure is long, there is a high risk of motion blur. If the time-exposure is short, the resulting image is noisy. The flutter shutter allows for arbitrarily long exposure... without blur. The article [1] (and its references) studies the method. As a byproduct, the study shows the existence of a class of linear filter capable of blindly removing moderate uniform motion blur.

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The goal of the project is to simulate image acquired by a flutter shutter camera and to test the validity of these filters on real images.

Encadrant:

Y. Tendero. Contact: yohann.tendero@telecom-paristech.fr.

Language de programmation:

Matlab, Octave, C/C++.

Article:

[1] Y. Tendero, "The Flutter Shutter Camera Simulator", Image Processing On Line, 2 (2012), pp. 225-242.
http://www.ipol.im/pub/art/2012/t-fscs/

Supports de cours