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Erschienen in:
2005 | OriginalPaper | Buchkapitel
An Improved Super-Resolution with Manifold Learning and Histogram Matching
verfasst von : Tak Ming Chan, Junping Zhang
Erschienen in: Advances in Biometrics
Verlag: Springer Berlin Heidelberg
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Biometric Person Authentication such as face, fingerprint, palmprint and signature depends on the quality of image processing. When it needs to be done under a low-resolution image, the accuracy will be impaired. So how to recover the lost information from downsampled images is important for both authentication and preprocessing. Based on Super-Resolution through Neighbor Embedding algorithm and histogram matching, we propose an improved super-resolution approach to choose more reasonable training images. First, the training image are selected by histogram matching. Second, neighbor embedding algorithm is employed to recover the high-resolution image. Experiments in several images show that our improved super-resolution approach is promising for potential applications such as low-resolution mobile phone or CCTV (Closed Circuit Television) image person authentication.