DocumentCode
2159634
Title
Textural Adaptive Learning-Based Super Resolution for Human Face Images
Author
Cao Yang ; Li Xiaoguang ; Li, Zhuo ; Shen Lansun
Author_Institution
Signal & Inf. Process. Lab., Beijing Univ. of Technol., Beijing, China
fYear
2009
fDate
17-19 Oct. 2009
Firstpage
1
Lastpage
3
Abstract
A low-resolution face image is segmented into detailed regions and flat regions. The detailed regions are super resolved using classified predictors according to the local textural structures, while, the flat regions are magnified using bilinear interpolation. Experimental results show that both the visual quality and the computational cost are improved.
Keywords
image resolution; image segmentation; image texture; interpolation; learning (artificial intelligence); bilinear interpolation; human face images; image segmentation; local textural structures; super resolution; textural adaptive learning; visual quality; Computational efficiency; Eyes; Face detection; Humans; Image reconstruction; Image resolution; Interpolation; Mouth; Nose; Signal resolution;
fLanguage
English
Publisher
ieee
Conference_Titel
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location
Tianjin
Print_ISBN
978-1-4244-4129-7
Electronic_ISBN
978-1-4244-4131-0
Type
conf
DOI
10.1109/CISP.2009.5304257
Filename
5304257
Link To Document