Fenglan Li

Work place: Chongqing University of Technology, Chongqing, 400054 China

E-mail: lifenglan@cqut.edu.cn

Website:

Research Interests: Image Compression, Image Manipulation, Image Processing, Information Systems, Information Theory

Biography

Fenglan Li was born in Sichuan province of China, in 1979. She received his B.S. degree in Sichuan Normal University in China. She is currently a postgraduate student in software engineering of College of Computer Sciences of Sichuan University in China. She is also currently a faculty member in library of Chongqing University of Technology in China. Her research interests include applied statistics and its applications in digital image processing, information processing, and literature evaluation. She has published over 10 papers including a few high-level SCI-indexed or EI-indexed journal papers.

Author Articles
A Hybrid Restoration Approach of Defocused Image Using MGAM and Inverse Filtering

By Fenglan Li Liyun Su Yun Jiang Min Sun

DOI: https://doi.org/10.5815/ijigsp.2013.08.03, Pub. Date: 28 Jun. 2013

A novel hybrid restoration scheme of defocused image is presented, which uses multivariate generalized additive model (MGAM) which is a nonparametric statistical regression model with no curse of dimensionality and inverse filtering (InvF). In this algorithm, firstly the five features of wavelet domain in defocused digital image, which are very stable relationship with the point spread function (PSF) parameter, are extracted by training and fitting a multivariate generalized additive model which is to estimate defocused blurred parameter. After the point spread function parameter is obtained, inverse filtering, which is needed to known the point spread function and a non-blind restoration method, is applied to complete the restoration for getting the true image. Simulated and real blurred images are experimentally illustrated to evaluate performances of the presented method. Results show that the proposed defocused image hybrid restoration technique is effective and robust.

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