Abstract
We proposed a recovery scheme for image deblurring. The scheme is under the framework of sparse representation and it has three main contributions. Firstly, considering the sparse property of natural image, the nonlocal overcompleted dictionaries are learned for image patches in our scheme. And, then, we coded the patches in each nonlocal clustering with the corresponding learned dictionary to recover the whole latent image. In addition, for some practical applications, we also proposed a method to evaluate the blur kernel to make the algorithm usable in blind image recovery. The experimental results demonstrated that the proposed scheme is competitive with some current state-of-the-art methods.
Highlights
Image recovery has been a widely studied issue in the past decades, which remains an active area in low-level image processing [1, 2]
The Gaussian blur kernel with the standard deviation 1.5 was used in our experiments and the additive white noise with different standard deviation was adopted
We addressed the image deblurring problem
Summary
Image recovery has been a widely studied issue in the past decades, which remains an active area in low-level image processing [1, 2]. The sparse regularization based modeling is employed to solve the recovery problems, which is proven to be more effective than the conventional regularization. Incorporating with the sparse representation, the NLM (nonlocal means) method is exploited in many image processing tasks successfully, such that [10, 11] all achieve the impressive recovery results based on this model. In this paper, inspired by the sparse representation and NLM techniques, we proposed a novel scheme for image deblurring, which will train many subdictionaries to better present the patches with different features and recover the whole latent image based on sparse model.
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