TY - GEN
T1 - Adaptive Screen Content Image Enhancement Strategy using Layer-based Segmentation
AU - Che, Zhaohui
AU - Zhai, Guangtao
AU - Gu, Ke
AU - Le Callet, Patrick
AU - Liu, Xianming
AU - Zhai, Deming
AU - Gu, Xiao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/26
Y1 - 2018/4/26
N2 - The ubiquitous screen content images (SCIs) play a significant role in various scenarios currently. However, most SCIs captured by consumer devices are frequently corrupted with distortions, especially contrast distortion. Unlike the natural images, SCIs are composed of text, graphics and natural scene pictures so that traditional image enhancement methods are not suitable for these compound images. Therefore, we innovatively proposed an adaptive strategy for enhancing SCIs in this paper. Firstly, we devised a segmentation method to divide SCI into text and pictorial regions. Next, the famous guided image filter (GIF) with big and small kernel sizes served as unsharpness masking for processing different regions adaptively. For verifying performance, the proposed method was tested on recently prevalent SCI datasets including SIQAD, and Webpage Dataset. Experimental results indicate that the proposed approach outperforms state-of-the-art methods in most SCIs with flat background.
AB - The ubiquitous screen content images (SCIs) play a significant role in various scenarios currently. However, most SCIs captured by consumer devices are frequently corrupted with distortions, especially contrast distortion. Unlike the natural images, SCIs are composed of text, graphics and natural scene pictures so that traditional image enhancement methods are not suitable for these compound images. Therefore, we innovatively proposed an adaptive strategy for enhancing SCIs in this paper. Firstly, we devised a segmentation method to divide SCI into text and pictorial regions. Next, the famous guided image filter (GIF) with big and small kernel sizes served as unsharpness masking for processing different regions adaptively. For verifying performance, the proposed method was tested on recently prevalent SCI datasets including SIQAD, and Webpage Dataset. Experimental results indicate that the proposed approach outperforms state-of-the-art methods in most SCIs with flat background.
KW - Screen content image
KW - autoregressive model
KW - guided filter
KW - image segmentation
KW - unsharpness masking
UR - https://www.scopus.com/pages/publications/85057110841
U2 - 10.1109/ISCAS.2018.8350911
DO - 10.1109/ISCAS.2018.8350911
M3 - 会议稿件
AN - SCOPUS:85057110841
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Y2 - 27 May 2018 through 30 May 2018
ER -