@inproceedings{0d58dda30c6a4aaa9c43bfcaa445841b,
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 = "Computer vision, Image denoising, 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.; null ; Conference date: 17-09-2017 Through 20-09-2017",
year = "2018",
month = feb,
day = "20",
doi = "10.1109/ICIP.2017.8296611",
language = "אנגלית",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "1895--1899",
booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",
address = "ארצות הברית",
}