An improved super-resolution with manifold learning and histogram matching

Authors:
Tak Ming Chan;Junping Zhang
Affiliations:
Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, China;Shanghai Key Laboratory of Intelligent Information Processing, Department of Computer Science and Engineering, Fudan University, China
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

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Abstract

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.