Image Deblurring Using Local Gaussian Models Based on Noise and Edge Distribution Estimation

Image deblurring is important to improve the quality of the image captured from a camera. To well deblur an image, it is an important issue to estimate the distributions of noise and edges, especially for the methods based on maximum likelihood estimation (MLE). In this paper, first, we propose a method for noise estimation based on the smooth prior. That is, for most of natural images, there should be some smooth regions. Then, after predicting the smooth regions and determining their gradient distributions, the noise distribution of the image can be well estimated. Then, based on the estimated noise and the additivity property of the Gaussian distributions, the local edge distributions can be determined. Then, the parameters of the MLE model can be assigned adaptively by the ratio of the standard deviations of edge and noise distributions. Experiments show that the proposed image deblurring algorithm based on the local Gaussian model and smooth prior has better performance than state-of-the-art image deblurring algorithms, including conventional and learning based ones.