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Real-time motion deblurring algorithm with robust noise suppression |
Hua-jun Feng*, Yong-pan Wang, Zhi-hai Xu, Qi Li, Hua Lei, Ju-feng Zhao |
State Key Lab of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China |
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Abstract In an image restoration process, to obtain good results is challenging because of the unavoidable existence of noise even if the blurring information is already known. To suppress the deterioration caused by noise during the image deblurring process, we propose a new deblurring method with a known kernel. First, the noise in the measurement process is assumed to meet the Gaussian distribution to fit the natural noise distribution. Second, the first and second orders of derivatives are supposed to satisfy the independent Gaussian distribution to control the non-uniform noise. Experimental results show that our method is obviously superior to the Wiener filter, regularized filter, and Richardson-Lucy (RL) algorithm. Moreover, owing to processing in the frequency domain, it runs faster than the other algorithms, in particular about six times faster than the RL algorithm.
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Received: 11 April 2009
Published: 28 April 2010
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Fund: Project supported by the National Natural Science Foundation of China (No. 60977010), the National Basic Research Program (973) of
China (No. 2009CB724006), and the National High-Tech Research and Development (863) Program of China (No. 2006AA12Z107) |
Real-time motion deblurring algorithm with robust noise suppression
In an image restoration process, to obtain good results is challenging because of the unavoidable existence of noise even if the blurring information is already known. To suppress the deterioration caused by noise during the image deblurring process, we propose a new deblurring method with a known kernel. First, the noise in the measurement process is assumed to meet the Gaussian distribution to fit the natural noise distribution. Second, the first and second orders of derivatives are supposed to satisfy the independent Gaussian distribution to control the non-uniform noise. Experimental results show that our method is obviously superior to the Wiener filter, regularized filter, and Richardson-Lucy (RL) algorithm. Moreover, owing to processing in the frequency domain, it runs faster than the other algorithms, in particular about six times faster than the RL algorithm.
关键词:
Motion blurring,
Motion kernel,
Gaussian distribution
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