Blind Single-Image Super-Resolution Reconstruction Based on Motion Blur

Abstract:

Blind image super-resolution reconstruction is one of the hot and difficult problem in image processing. a framework of blind single-image super-resolution reconstruction algorithm is proposed. In the low-resolution imaging model, the processes of motion blur, down-sampling, and noise are considered. The parameter of motion blur is estimated through an error-parameter analysis method. Utilizing Wiener filtering image restoration algorithm, an error-parameter curve at different motion distance is generated, from which the motion distance of the motion point spread function (PSF) can be estimated approximately. The super-resolution image is reconstructed through the iterative back projection (IBP) algorithm. The experimental results show that motion PSF estimation plays an important role on the quality of the SR reconstructed image, and also demonstrate the effectiveness of the proposed algorithm.

• Periodical: Advanced Materials Research (Volumes 225-226)
• Pages: 895-899
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.225-226.895
• Online since: April 2011
• Authors: Feng Qing Qin, Zhong Li, Li Hong Zhu, Ying De You, Li Lan Cao
• Keywords: Error-Parameter Analysis, Motion Blur, Point Spread Function, Single-Image, Super-Resolution

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