Blind image deblurring via coupled sparse representation

• A blind image deblurring algorithm proposed by learning coupled dictionaries with sparse coding.
• Coupled sparse representation of blurry image and its latent patch investigated.
• Unified framework for optimization and fast learning scheme proposed for learning dictionaries.
• Efficiency of the algorithm evaluated on standard images against state-of-the-art algorithms.

The problem of blind image deblurring is more challenging than that of non-blind image deblurring, due to the lack of knowledge about the point spread function in the imaging process. In this paper, a learning-based method of estimating blur kernel under the ℓ0ℓ0 regularization sparsity constraint is proposed for blind image deblurring. Specifically, we model the patch-based matching between the blurred image and its sharp counterpart via a coupled sparse representation. Once the blur kernel is obtained, a non-blind deblurring algorithm can be applied to the final recovery of the sharp image. Our experimental results show that the visual quality of restored sharp images is competitive with the state-of-the-art algorithms for both synthetic and real images.