Enhanced Sparse Model for Blind Deblurring

Existing arts have shown promising efforts to deal with the blind deblurring task. However, most of the recent works assume the additive noise involved in the blurring process to be simple-distributed (i.e. Gaussian or Laplacian), while the real-world case is proved to be much more complicated. In this paper, we develop a new term to better fit the complex natural noise. Specifically, we use a combination of a dense function (i.e. l₂) and a newly designed enhanced sparse model termed as l_e, which is developed from two sparse models (i.e. l₁ and l₀), to fulfill the task. Moreover, we further suggest using l_e to regularize image gradients. Compared to the widely-adopted l₀ sparse term, l_e can penalize more insignificant image details (Fig. 1). Based on the half-quadratic splitting method, we provide an effective scheme to optimize the overall formulation. Comprehensive evaluations on public datasets and real-world images demonstrate the superiority of the proposed method against state-of-the-art methods in terms of both speed and accuracy.