Deep class-aware image denoising

Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The increasing demand for high image quality in mobile devices brings forth the need for better computational enhancement techniques, and image denoising in particular. To this end, we propose a new fully convolutional deep neural network architecture which is simple yet powerful and achieves state-of-the-art performance for additive Gaussian noise removal. Furthermore, we claim that the personal photo-collections can usually be categorized into a small set of semantic classes. However simple, this observation has not been exploited in image denoising until now. We show that a significant boost in performance of up to 0.4dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class. Relying on the hugely successful existing image classifiers, this research advocates for using a class-aware approach in all image enhancement tasks.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509021758
StatePublished - 2 Jul 2017
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference24th IEEE International Conference on Image Processing, ICIP 2017


  • Computer vision
  • Image denoising
  • Image enhancement
  • Image processing
  • Machine learning


Dive into the research topics of 'Deep class-aware image denoising'. Together they form a unique fingerprint.

Cite this