Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Peleg, Tomer"'
Image demosaicing is an important step in the image processing pipeline for digital cameras. In data centric approaches, such as deep learning, the distribution of the dataset used for training can impose a bias on the networks' outcome. For example,
Externí odkaz:
http://arxiv.org/abs/2303.15792
Transferring the absolute depth prediction capabilities of an estimator to a new domain is a task with significant real-world applications. This task is specifically challenging when images from the new domain are collected without ground-truth depth
Externí odkaz:
http://arxiv.org/abs/2303.07662
General object detectors use powerful backbones that uniformly extract features from images for enabling detection of a vast amount of object types. However, utilization of such backbones in object detection applications developed for specific object
Externí odkaz:
http://arxiv.org/abs/2107.10050
Publikováno v:
IEEE Transactions on Information Theory, 60(12):7928-7946, December 2014
This paper focuses on characterizing the fundamental performance limits that can be expected from an ideal decoder given a general model, ie, a general subset of "simple" vectors of interest. First, we extend the so-called notion of instance optimali
Externí odkaz:
http://arxiv.org/abs/1311.6239
Autor:
Peleg, Tomer, Elad, Michael
The co-sparse analysis model for signals assumes that the signal of interest can be multiplied by an analysis dictionary \Omega, leading to a sparse outcome. This model stands as an interesting alternative to the more classical synthesis based sparse
Externí odkaz:
http://arxiv.org/abs/1203.2769
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in which the signal is assumed to be generated as a combination of a few
Externí odkaz:
http://arxiv.org/abs/1010.5734
Self-supervised monocular depth estimators can be trained or fine-tuned on new scenes using only images and no ground-truth depth data, achieving good accuracy. However, these estimators suffer from the inherent ambiguity of the depth scale, signific
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c12d36c3cea532aca8b7309380ea6dd6
http://arxiv.org/abs/2303.07662
http://arxiv.org/abs/2303.07662
Image demosaicing is an important step in the image processing pipeline for digital cameras, and it is one of the many tasks within the field of image restoration. A well-known characteristic of natural images is that most patches are smooth, while h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5fdd3855480a7451c2353d8e3b86f918
Publikováno v:
21st European Signal Processing Conference (EUSIPCO 2013)
21st European Signal Processing Conference (EUSIPCO 2013), Sep 2013, Marrakech, Morocco
21st European Signal Processing Conference (EUSIPCO 2013), Sep 2013, Marrakech, Morocco
to appear in EUSIPCO 2013; International audience; We propose a theoretical study of the conditions guaranteeing that a decoder will obtain an optimal signal recovery from an underdetermined set of linear measurements. This special type of performanc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0a10f65d016c4f26be80a90ae55cf1e
https://hal.inria.fr/hal-00812858
https://hal.inria.fr/hal-00812858
Publikováno v:
Signal Processing with Adaptive Sparse Structured Representations 2013 (2013)
Signal Processing with Adaptive Sparse Structured Representations 2013 (2013), EPFL, Jul 2013, Lausanne, Switzerland
Signal Processing with Adaptive Sparse Structured Representations 2013 (2013), EPFL, Jul 2013, Lausanne, Switzerland
International audience; We propose a theoretical study of the conditions guar- anteeing that a decoder will obtain an optimal signal recovery from an underdetermined set of linear measurements. This special type of performance guarantee is termed ins
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ae95d7b235a897bf396661e6013a050c
https://inria.hal.science/hal-00811673
https://inria.hal.science/hal-00811673