---
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'
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'
...