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Steerable pyramid-based face hallucination
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DOI：10.1016/j.patcog.2004.11.007.png)
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Abstract
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In this paper we propose a robust learning-based face hallucination algorithm, which predicts a high-resolution face image from an input low-resolution image. It can be utilized for many computer vision tasks, such as face recognition and face tracking. With the help of a database of other high-resolution face images, we use a steerable pyramid to extract multi-orientation and multi-scale information of local low-level facial features both from the input low-resolution face image and other high-resolution ones, and use a pyramid-like parent structure and local best match approach to estimate the best prior; then, this prior is incorporated into a Bayesian maximum a posterior (MAP) framework, and finally the high-resolution version is optimized by a steepest decent algorithm. The experimental results show that we can enhance a 24 x 32 face image into a 96 x 128 one while the visual effect is relatively good. (c) 2004 Published by Elsevier Ltd on behalf of Pattern Recognition Society.
Keywords:
face hallucination
super-resolution
steerable pyramid
local best match
Bayesian maximum a posterior (MAP)
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