| Summary: | 碩士 === 國立臺灣大學 === 電子工程學研究所 === 102 === In surveillance system, to recognize the human face is always the most principal target. Due to the low-quality sensor of surveillance camera and the compression by video coding, the captured facial images are usually in low-resolution. In order to reach a better face recognition rate, a better resolution of these facial images is needed. Besides enhance the quality of sensor or raise the performance of video coding, to enhance the resolution of desired facial images, the face hallucination can be applied.
Face hallucination is a super resolution process targeting on facial images. It can recover the related high-resolution image with rich details from a low-resolution facial image.
Therefore, the goal of our work is to improve the resolution targeting on low-resolution facial images. The corresponded hardware design is also provided.
We propose a low complexity face hallucination algorithm called eigen-patch which can provide high-resolution facial images with rich details and sharpness. Our eigen-patch algorithm combine the eigen-transformation face hallucination with the structure of position-patch based face hallucination.
This algorithm has two main contributions. First is conductiing the eigen-transformation on patch size. The eigen transformation raise the image quality and reduce the computational complexity without solving the least square problem.
The second contribution is the input image alignment skill. In usual case, the input low-resolution image would not be well-aligned. Therefore, the result high image will suffer from the artifacts and significant quality degradation. Based on the input low-resolution image, the input image alignment mechanism open a search range on database image in order to reach a better alignment. Experimental reuslts shows that the proposed face hallucination algorithm performs better than other ones.
In hardware architecture design, we also simplify the original Eigen-Patch algorithm. We shift the image alignm mechanism into an earlier position, as a result, we do not have to recover all the facial images of different position. Only the correct aligned one will be hallucinated. The new Eigen-Patch scheme reduce the system bandwidth. We also analysis the number of database images in order to reduce the number of database images. The reduction of database images can further decrease the system bandwidth. Finally, our hardware implementation can reach a 4 times hallucination with 30 X 25 input image in 30fps.
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