An improved blind restoration algorithm for multiframe turbulence-degraded images
This paper proposes an improved blind deconvolution algorithm, which adopts maximum likelihood method to find the most similar estimation of the PSF and object with Poisson-based probability model. The algorithm integrates Cauchy probability distribution model into the estimation of the PSF under the condition of low SNR, uses the characteristic of short-exposure image sequence that the adjacent images have similar PSF to get restored image with frames as few as possible. The experimental results show that this method is robust with high ability of resisting noise in the restoration of turbulence-degraded images.
blind deconvolution turbulence-degraded image maximum likelihood estimation Cauchy distribution
Jing Guan Jianchong chen Kejia Yi Ze Wang
State Key Laboratory for Multispectral Information Processing TechnologiesInstitute for Pattern Reco College of Computer Science and Technology, Harbin Engineering University, Harbin, China, 150001
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）