Search

Your search keyword '"Le Pendu, Mikael"' showing total 31 results
Start Over Author "Le Pendu, Mikael"
31 results on '"Le Pendu, Mikael"'

2. List of contributors

Author

Ak, Ali, primary, Alain, Martin, additional, Alaya Cheikh, Faouzi, additional, Alexiou, Evangelos, additional, Battisti, Federica, additional, Bätz, Michel, additional, Cagnazzo, Marco, additional, Cesar, Pablo, additional, Chao, Fang-Yi, additional, Chelli, Kelvin, additional, Chen, Siheng, additional, Croci, Simone, additional, Dufaux, Frédéric, additional, Eisert, Peter, additional, Feldmann, Ingo, additional, Fink, Laura, additional, Fößel, Siegfried, additional, Forchhammer, Søren, additional, Fremerey, Stephan, additional, Garus, Patrick, additional, Goldmann, Florian, additional, Graziosi, Danillo, additional, Guedes, Alan, additional, Gul, Muhammad Shahzeb Khan, additional, Hellge, Cornelius, additional, Helzle, Volker, additional, Herfet, Thorsten, additional, Hilsmann, Anna, additional, Jaschke, Tobias, additional, Jung, Joël, additional, Keinert, Joachim, additional, Krivokuća, Maja, additional, Lavoué, Guillaume, additional, Lebreton, Pierre, additional, Le Callet, Patrick, additional, Le Pendu, Mikael, additional, Li, Jie, additional, Liu, Jingyu, additional, Mantel, Claire, additional, Mantiuk, Rafał K., additional, Marvie, Jean-Eudes, additional, Maugey, Thomas, additional, Milovanović, Marta, additional, Nehmé, Yana, additional, O'Dwyer, Néill, additional, Ozcinar, Cagri, additional, Palomar, Rafael, additional, Pang, Jiahao, additional, Pelanis, Egidijus, additional, Prappacher, Nico, additional, Prasanna Kumar, Rahul, additional, Quach, Maurice, additional, Raake, Alexander, additional, Rossi, Silvia, additional, Schreer, Oliver, additional, Singla, Ashutosh, additional, Smolic, Aljosa, additional, Stepanov, Milan, additional, Tian, Dong, additional, Toni, Laura, additional, Valenzise, Giuseppe, additional, Viola, Irene, additional, Wang, Congcong, additional, Young, Gareth W., additional, Zeng, Jin, additional, Zerman, Emin, additional, Zhong, Fangcheng, additional, and Ziegler, Matthias, additional

Published
2023
Full Text
View/download PDF

5. Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration Mikael

Author

Le Pendu, Mikael, Guillemot, Christine, Analysis representation, compression and communication of visual data (Sirocco), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), ANR-19-CHIA-0007,DeepCIM,Apprentissage profond pour imagerie computationnelle avec nouvelles modalités d'images(2019), and European Project: 694122,H2020 ERC,ERC-2015-AdG,CLIM(2016)

Subjects
Plug-and-Play Prior, Interpolation MSC codes. 62H35, Denoising, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Inverse Problems, 68U10, 68T99, Preconditioning, ADMM, 94A08
Abstract

International audience; Plug-and-Play priors recently emerged as a powerful technique for solving inverse problems by plugging a denoiser into a classical optimization algorithm. The denoiser accounts for the regularization and therefore implicitly determines the prior knowledge on the data, hence replacing typical handcrafted priors. In this paper, we extend the concept of Plug-and-Play priors to use denoisers that can be parameterized for non-constant noise variance. In that aim, we introduce a preconditioning of the ADMM algorithm, which mathematically justifies the use of such an adjustable denoiser. We additionally propose a procedure for training a convolutional neural network for high quality non-blind image denoising that also allows for pixel-wise control of the noise standard deviation. We show that our pixel-wise adjustable denoiser, along with a suitable preconditioning strategy, can further improve the Plug-and-Play ADMM approach for several applications, including image completion, interpolation, demosaicing and Poisson denoising.

Published
2022

8. Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration.

Author

Le Pendu, Mikael and Guillemot, Christine

Subjects
IMAGE reconstruction, CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, INVERSE problems, IMAGE denoising, NOISE control
Abstract

Plug-and-Play priors recently emerged as a powerful technique for solving inverse problems by plugging a denoiser into a classical optimization algorithm. The denoiser accounts for the regularization and therefore implicitly determines the prior knowledge on the data, hence replacing typical handcrafted priors. In this paper, we extend the concept of Plug-and-Play priors to use denoisers that can be parameterized for nonconstant noise variance. In that aim, we introduce a preconditioning of the ADMM algorithm, which mathematically justifies the use of such an adjustable denoiser. We additionally propose a procedure for training a convolutional neural network for high quality nonblind image denoising that also allows for pixelwise control of the noise standard deviation. We show that our pixelwise adjustable denoiser, along with a suitable preconditioning strategy, can further improve the Plug-and-Play ADMM approach for several applications, including image completion, interpolation, demosaicing, and Poisson denoising. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

31. Adaptive Re-Quantization For High Dynamic Range Video Compression

Author

Mikael Le Pendu, Dominique Thoreau, Christine Guillemot, Analysis representation, compression and communication of visual data (Sirocco ), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Technicolor R & I [Cesson Sévigné], Technicolor, Le Pendu, Mikael, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Floating point, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, High-dynamic-range video, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Color depth, 0202 electrical engineering, electronic engineering, information engineering, Codec, Computer vision, Quantization (image processing), ACM: A.: General Literature/A.0: GENERAL/A.0.1: Conference proceedings, High dynamic range, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Lossless compression, Pixel, business.industry, Dynamic range, Quantization (signal processing), 020207 software engineering, computer.file_format, Video compression picture types, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, Image file formats, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Data compression
Abstract

International audience; High Dynamic Range (HDR) images contain more intensity levels than traditional image formats. Instead of 8 or 10 bit integers, floating point values are generally used to represent the pixel data. To extend the use of existing video codecs such as HEVC to HDR floating point video sequences, we propose a method that converts the floating point data and reduces the bit depth of input images with minimal loss. Several variants of the method are proposed. They are adapted to different quality requirements. In particular, near lossless compression is addressed.

Published
2014

Catalog

Books, media, physical & digital resources
See catalog results