A time dependent model for deblurring and denoising problems is proposed.
介绍一种依赖时间的新模型来解决图像除噪音和除模糊问题。
We use a new deblurring function that is easier to implement than inverse filter.
采用了新的消模糊函数，它比起逆滤波器较易于制作。
To overcome this difficulty, a new local deblurring algorithm is proposed in this paper.
为了克服这个困难，本文提出一种新的局部图像复原算法；
An improved spatial domain image deblurring algorithm is proposed based on wiener filter and deconvolution.
改进并提出了基于频域维纳滤波器方法的空域图象模糊复原算法。
Theoretical analysis shows that the new deblurring function is the Fourier transform of inverse filtering function.
理论分析证明，此新消模糊函数是逆滤波函数的傅里叶变换。
Influences on the deblurring results by different choice of Gain Map parameters are discussed. It chooses reasonable parameters to achieve the best results.
详细讨论了引入的增益图的不同参数选择对去模糊结果的影响，通过选取合理的参数得到最佳的结果。
This thesis focuses on the image restoration in Bayesian framework, which mainly contains research on model parameter estimation, image denoising, and image deblurring.
本论文研究的重点是贝叶斯框架下的图像恢复问题，包括了参数估计、图像降噪和图像去模糊等。
This thesis focuses on the image restoration in Bayesian framework, which mainly contains research on model parameter estimation, image denoising, and image deblurring.
本论文研究的重点是贝叶斯框架下的图像恢复问题，包括了参数估计、图像降噪和图像去模糊等。
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