---
OA_place: repository
OA_type: green
_id: '18271'
abstract:
- lang: eng
  text: We propose a fully convolutional neural-network architecture for image denoising
    which is simple yet powerful. Its structure allows to exploit the gradual nature
    of the denoising process, in which the shallow layers handle local noise statistics,
    while deeper layers recover edges and enhance textures. Our method advances the
    state of the art when trained for different noise levels and distributions (both
    Gaussian and Poisson). In addition, we show that making the denoiser class-aware
    by exploiting semantic class information boosts the performance, enhances the
    textures, and reduces the artifacts.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Tal
  full_name: Remez, Tal
  last_name: Remez
- first_name: Or
  full_name: Litany, Or
  last_name: Litany
- first_name: Raja
  full_name: Giryes, Raja
  last_name: Giryes
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
citation:
  ama: Remez T, Litany O, Giryes R, Bronstein AM. Class-aware fully convolutional
    Gaussian and Poisson denoising. <i>IEEE Transactions on Image Processing</i>.
    2018;27(11):5707-5722. doi:<a href="https://doi.org/10.1109/tip.2018.2859044">10.1109/tip.2018.2859044</a>
  apa: Remez, T., Litany, O., Giryes, R., &#38; Bronstein, A. M. (2018). Class-aware
    fully convolutional Gaussian and Poisson denoising. <i>IEEE Transactions on Image
    Processing</i>. Institute of Electrical and Electronics Engineers. <a href="https://doi.org/10.1109/tip.2018.2859044">https://doi.org/10.1109/tip.2018.2859044</a>
  chicago: Remez, Tal, Or Litany, Raja Giryes, and Alex M. Bronstein. “Class-Aware
    Fully Convolutional Gaussian and Poisson Denoising.” <i>IEEE Transactions on Image
    Processing</i>. Institute of Electrical and Electronics Engineers, 2018. <a href="https://doi.org/10.1109/tip.2018.2859044">https://doi.org/10.1109/tip.2018.2859044</a>.
  ieee: T. Remez, O. Litany, R. Giryes, and A. M. Bronstein, “Class-aware fully convolutional
    Gaussian and Poisson denoising,” <i>IEEE Transactions on Image Processing</i>,
    vol. 27, no. 11. Institute of Electrical and Electronics Engineers, pp. 5707–5722,
    2018.
  ista: Remez T, Litany O, Giryes R, Bronstein AM. 2018. Class-aware fully convolutional
    Gaussian and Poisson denoising. IEEE Transactions on Image Processing. 27(11),
    5707–5722.
  mla: Remez, Tal, et al. “Class-Aware Fully Convolutional Gaussian and Poisson Denoising.”
    <i>IEEE Transactions on Image Processing</i>, vol. 27, no. 11, Institute of Electrical
    and Electronics Engineers, 2018, pp. 5707–22, doi:<a href="https://doi.org/10.1109/tip.2018.2859044">10.1109/tip.2018.2859044</a>.
  short: T. Remez, O. Litany, R. Giryes, A.M. Bronstein, IEEE Transactions on Image
    Processing 27 (2018) 5707–5722.
date_created: 2024-10-09T07:42:49Z
date_published: 2018-11-01T00:00:00Z
date_updated: 2024-10-16T13:00:30Z
day: '01'
doi: 10.1109/tip.2018.2859044
extern: '1'
external_id:
  arxiv:
  - '1808.06562'
fulldoi: https://doi.org/10.1109/tip.2018.2859044
intvolume: '        27'
issue: '11'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.1808.06562
month: '11'
oa: 1
oa_version: Preprint
page: 5707-5722
publication: IEEE Transactions on Image Processing
publication_identifier:
  eissn:
  - 1941-0042
  issn:
  - 1057-7149
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Class-aware fully convolutional Gaussian and Poisson denoising
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 27
year: '2018'
...
---
OA_type: closed access
_id: '3695'
abstract:
- lang: eng
  text: We give an analytical and geometrical treatment of what it means to separate
    a Gaussian kernel along arbitrary axes in Ropfn, and we present a separation scheme
    that allows us to efficiently implement anisotropic Gaussian convolution filters
    for data of arbitrary dimensionality. Based on our previous analysis we show that
    this scheme is optimal with regard to the number of memory accesses and interpolation
    operations needed. The proposed method relies on nonorthogonal convolution axes
    and works completely in image space. Thus, it avoids the need for a fast Fourier
    transform (FFT)-subroutine. Depending on the accuracy and speed requirements,
    different interpolation schemes and methods to implement the one-dimensional Gaussian
    (finite impulse response and infinite impulse response) can be integrated. Special
    emphasis is put on analyzing the performance and accuracy of the new method. In
    particular, we show that without any special optimization of the source code,
    it can perform anisotropic Gaussian filtering faster than methods relying on the
    FFT.
article_processing_charge: No
article_type: original
author:
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Oliver
  full_name: Wirjadi, Oliver
  last_name: Wirjadi
citation:
  ama: Lampert C, Wirjadi O. An optimal non-orthogonal separation of the anisotropic
    Gaussian convolution filter. <i>IEEE Transactions on Image Processing (TIP)</i>.
    2006;15(11):3501-3513. doi:<a href="https://doi.org/ 10.1109/TIP.2006.877501 ">
    10.1109/TIP.2006.877501 </a>
  apa: Lampert, C., &#38; Wirjadi, O. (2006). An optimal non-orthogonal separation
    of the anisotropic Gaussian convolution filter. <i>IEEE Transactions on Image
    Processing (TIP)</i>. IEEE. <a href="https://doi.org/ 10.1109/TIP.2006.877501
    ">https://doi.org/ 10.1109/TIP.2006.877501 </a>
  chicago: Lampert, Christoph, and Oliver Wirjadi. “An Optimal Non-Orthogonal Separation
    of the Anisotropic Gaussian Convolution Filter.” <i>IEEE Transactions on Image
    Processing (TIP)</i>. IEEE, 2006. <a href="https://doi.org/ 10.1109/TIP.2006.877501
    ">https://doi.org/ 10.1109/TIP.2006.877501 </a>.
  ieee: C. Lampert and O. Wirjadi, “An optimal non-orthogonal separation of the anisotropic
    Gaussian convolution filter,” <i>IEEE Transactions on Image Processing (TIP)</i>,
    vol. 15, no. 11. IEEE, pp. 3501–3513, 2006.
  ista: Lampert C, Wirjadi O. 2006. An optimal non-orthogonal separation of the anisotropic
    Gaussian convolution filter. IEEE Transactions on Image Processing (TIP). 15(11),
    3501–3513.
  mla: Lampert, Christoph, and Oliver Wirjadi. “An Optimal Non-Orthogonal Separation
    of the Anisotropic Gaussian Convolution Filter.” <i>IEEE Transactions on Image
    Processing (TIP)</i>, vol. 15, no. 11, IEEE, 2006, pp. 3501–13, doi:<a href="https://doi.org/
    10.1109/TIP.2006.877501 "> 10.1109/TIP.2006.877501 </a>.
  short: C. Lampert, O. Wirjadi, IEEE Transactions on Image Processing (TIP) 15 (2006)
    3501–3513.
date_created: 2018-12-11T12:04:40Z
date_published: 2006-11-01T00:00:00Z
date_updated: 2026-08-27T11:04:53Z
day: '01'
doi: ' 10.1109/TIP.2006.877501 '
extern: '1'
external_id:
  pmid:
  - '17076408 '
fulldoi: 'https://doi.org/ 10.1109/TIP.2006.877501 '
intvolume: '        15'
issue: '11'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:386-kluedo-14003
month: '11'
oa: 1
oa_version: None
page: 3501 - 3513
pmid: 1
publication: IEEE Transactions on Image Processing (TIP)
publication_identifier:
  eissn:
  - 1941-0042
  issn:
  - 1057-7149
publication_status: published
publisher: IEEE
publist_id: '2669'
status: public
title: An optimal non-orthogonal separation of the anisotropic Gaussian convolution
  filter
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 15
year: '2006'
...