A Fast Algorithm for Learning-based Super- resolution Reconstruction of Face Image
Considering that the interpolation method for image zooming yields blocky or blurred results and the results of learning-based super-resolution algorithm are satisfactory but time-consuming, in this paper, we introduce a novel fast algorithm for learningbased super-resolution reconstruction of face image and implement it. First, we generate an optimized training database; second, for each test lowresolution color face image, we locate the skin region of YIQ spaces Y channel and apply superresolution reconstruction algorithm to this region of interest; third, we break it into patches, search for N closest candidates for each patch, and under maximum a posteriori criterion, get best-matching candidate for each patch; finally, we get residual details by merging all patches, and then me final reconstructed high-resolution face image after integrating it with interpolated results.
maximum a posterior skin region location learning-based super-resolution
Liang Wu Xingang Wang
Institute of Automation, Chinese Academy of Sciences Beijing, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
1063-1067
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）