Coarse-to-fine blind image deblurring based on K-means clustering.

Blind image deblurring is a challenging image processing problem, and a proper solution for this problem has many applications in the real world. This is an ill-posed problem, as both the sharp image and blur kernel are unknown. The traditional methods based on maximum a posterior (MAP) apply heavy constraints on the latent image or blur kernel to find the solution. However, these constraints are not always effective; meanwhile, they are very time-consuming. Recently, new approaches based on deep learning have emerged. The methods based on this approach suffer from two problems: the need for a large number of images and kernels for training and also the dependency of the result on the training data. In this paper, we propose a multiscale method based on MAP framework for image motion deblurring. In this method, we represent the blurry image in different scales. We suggest segmenting the image of each scale using κ -means clustering. Using the image information at dominant edges guided by the segmented images, the blur kernel is estimated at each scale. The blur kernel at the finest level of the pyramid is estimated from the coarser levels in a coarse-to-fine manner. Unlike the existing MAP-based methods, the proposed method does not need mathematically complicated assumptions to estimate the intermediate latent image. So the proposed image deblurring is run fast. We evaluated the proposed method and compared it to the existing methods. The experimental results on real and synthetic blurry images demonstrate that the proposed scheme has promising results. The proposed method competes with the existing MAP-based methods for reconstructing qualitative sharp images, while the execution time for our method is considerably less. [ABSTRACT FROM AUTHOR]