@inproceedings{689268d1e4174df9ba76241e93c9e189,
title = "Deep class-aware image denoising",
abstract = "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.",
keywords = "Image denoising, computer vision, image enhancement, image processing, machine learning",
author = "Tal Remez and Or Litany and Raja Giryes and Bronstein, {Alex M.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 12th International Conference on Sampling Theory and Applications, SampTA 2017 ; Conference date: 03-07-2017 Through 07-07-2017",
year = "2017",
month = sep,
day = "1",
doi = "10.1109/SAMPTA.2017.8024474",
language = "אנגלית",
series = "2017 12th International Conference on Sampling Theory and Applications, SampTA 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "138--142",
editor = "Gholamreza Anbarjafari and Andi Kivinukk and Gert Tamberg",
booktitle = "2017 12th International Conference on Sampling Theory and Applications, SampTA 2017",
address = "ארצות הברית",
}