TY - GEN
T1 - Visualizing image priors
AU - Rott Shaham, Tamar
AU - Michaeli, Tomer
N1 - Publisher Copyright: © Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Image priors play a key role in low-level vision tasks. Over the years, many priors have been proposed, based on a wide variety of principles. While different priors capture different geometric properties, there is currently no unified approach to interpreting and comparing priors of different nature. This limits our ability to analyze failures or successes of image models in specific settings, and to identify potential improvements. In this paper, we introduce a simple technique for visualizing image priors. Our method determines how images should be deformed so as to best conform to a given image model. The deformed images constructed this way, highlight the elementary geometric structures to which the prior resonates. We use our approach to study various popular image models, and reveal interesting behaviors, which were not noticed in the past. We confirm our findings through denoising experiments. These validate that the structures we reveal as ‘optimal’ for a specific prior are indeed better denoised by this prior.
AB - Image priors play a key role in low-level vision tasks. Over the years, many priors have been proposed, based on a wide variety of principles. While different priors capture different geometric properties, there is currently no unified approach to interpreting and comparing priors of different nature. This limits our ability to analyze failures or successes of image models in specific settings, and to identify potential improvements. In this paper, we introduce a simple technique for visualizing image priors. Our method determines how images should be deformed so as to best conform to a given image model. The deformed images constructed this way, highlight the elementary geometric structures to which the prior resonates. We use our approach to study various popular image models, and reveal interesting behaviors, which were not noticed in the past. We confirm our findings through denoising experiments. These validate that the structures we reveal as ‘optimal’ for a specific prior are indeed better denoised by this prior.
UR - http://www.scopus.com/inward/record.url?scp=84990031680&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-46466-4_9
DO - https://doi.org/10.1007/978-3-319-46466-4_9
M3 - منشور من مؤتمر
SN - 9783319464657
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 136
EP - 153
BT - Computer Vision - 14th European Conference, ECCV 2016, Proceedings
A2 - Leibe, Bastian
A2 - Matas, Jiri
A2 - Sebe, Nicu
A2 - Welling, Max
T2 - 14th European Conference on Computer Vision, ECCV 2016
Y2 - 8 October 2016 through 16 October 2016
ER -