Instagram filter removal on fashionable images

Name: Instagram filter removal on fashionable images
Author: Kınlı, Osman Furkan, Özcan, Barış, Kıraç, Mustafa Furkan

İsim	Instagram filter removal on fashionable images
Yazar	Kınlı, Osman Furkan, Özcan, Barış, Kıraç, Mustafa Furkan
Basım Tarihi:	2021-06
Basım Yeri	- IEEE
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-6654-4899-4
Kayıt Numarası	9c51cb2a-139e-4ca3-b4da-1e0a54447536
Lokasyon	Computer Science
Tarih	2021-06
Örnek Metin	Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.
DOI	10.1109/CVPRW53098.2021.00083

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Instagram filter removal on fashionable images

Yazar Kınlı, Osman Furkan, Özcan, Barış, Kıraç, Mustafa Furkan

Basım Tarihi 2021-06

Basım Yeri - IEEE

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-6654-4899-4

Kayıt Numarası 9c51cb2a-139e-4ca3-b4da-1e0a54447536

Lokasyon Computer Science

Tarih 2021-06

Örnek Metin Social media images are generally transformed by filtering to obtain aesthetically more pleasing appearances. However, CNNs generally fail to interpret both the image and its filtered version as the same in the visual analysis of social media images. We introduce Instagram Filter Removal Network (IFRNet) to mitigate the effects of image filters for social media analysis applications. To achieve this, we assume any filter applied to an image substantially injects a piece of additional style information to it, and we consider this problem as a reverse style transfer problem. The visual effects of filtering can be directly removed by adaptively normalizing external style information in each level of the encoder. Experiments demonstrate that IFRNet outperforms all compared methods in quantitative and qualitative comparisons, and has the ability to remove the visual effects to a great extent. Additionally, we present the filter classification performance of our proposed model, and analyze the dominant color estimation on the images unfiltered by all compared methods.

DOI 10.1109/CVPRW53098.2021.00083