{"status":"public","day":"22","_id":"18402","citation":{"apa":"Remez, T., Litany, O., Giryes, R., &#38; Bronstein, A. M. (2018). Deep class-aware image denoising. In <i>2017 IEEE International Conference on Image Processing (ICIP)</i> (pp. 1895–1899). Beijing, China: IEEE. <a href=\"https://doi.org/10.1109/icip.2017.8296611\">https://doi.org/10.1109/icip.2017.8296611</a>","chicago":"Remez, Tal, Or Litany, Raja Giryes, and Alex M. Bronstein. “Deep Class-Aware Image Denoising.” In <i>2017 IEEE International Conference on Image Processing (ICIP)</i>, 1895–99. IEEE, 2018. <a href=\"https://doi.org/10.1109/icip.2017.8296611\">https://doi.org/10.1109/icip.2017.8296611</a>.","mla":"Remez, Tal, et al. “Deep Class-Aware Image Denoising.” <i>2017 IEEE International Conference on Image Processing (ICIP)</i>, IEEE, 2018, pp. 1895–99, doi:<a href=\"https://doi.org/10.1109/icip.2017.8296611\">10.1109/icip.2017.8296611</a>.","ama":"Remez T, Litany O, Giryes R, Bronstein AM. Deep class-aware image denoising. In: <i>2017 IEEE International Conference on Image Processing (ICIP)</i>. IEEE; 2018:1895-1899. doi:<a href=\"https://doi.org/10.1109/icip.2017.8296611\">10.1109/icip.2017.8296611</a>","ieee":"T. Remez, O. Litany, R. Giryes, and A. M. Bronstein, “Deep class-aware image denoising,” in <i>2017 IEEE International Conference on Image Processing (ICIP)</i>, Beijing, China, 2018, pp. 1895–1899.","ista":"Remez T, Litany O, Giryes R, Bronstein AM. 2018. Deep class-aware image denoising. 2017 IEEE International Conference on Image Processing (ICIP). 24th IEEE International Conference on Image Processing, 1895–1899.","short":"T. Remez, O. Litany, R. Giryes, A.M. Bronstein, in:, 2017 IEEE International Conference on Image Processing (ICIP), IEEE, 2018, pp. 1895–1899."},"article_processing_charge":"No","quality_controlled":"1","abstract":[{"text":"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.","lang":"eng"}],"date_published":"2018-02-22T00:00:00Z","oa_version":"None","title":"Deep class-aware image denoising","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publisher":"IEEE","language":[{"iso":"eng"}],"publication_status":"published","publication":"2017 IEEE International Conference on Image Processing (ICIP)","conference":{"start_date":"2017-09-17","end_date":"2017-09-20","name":"24th IEEE International Conference on Image Processing","location":"Beijing, China"},"page":"1895 - 1899","author":[{"full_name":"Remez, Tal","first_name":"Tal","last_name":"Remez"},{"first_name":"Or","full_name":"Litany, Or","last_name":"Litany"},{"last_name":"Giryes","first_name":"Raja","full_name":"Giryes, Raja"},{"last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","first_name":"Alexander","orcid":"0000-0001-9699-8730","full_name":"Bronstein, Alexander"}],"year":"2018","publication_identifier":{"eissn":["2381-8549"],"isbn":["9781509021765"]},"doi":"10.1109/icip.2017.8296611","extern":"1","date_created":"2024-10-15T11:20:54Z","type":"conference","date_updated":"2024-12-05T14:00:53Z","month":"02","scopus_import":"1"}