  Blind image deblurring is a long standing challenging problem in image
processing and low-level vision. Recently, sophisticated priors such as dark
channel prior, extreme channel prior, and local maximum gradient prior, have
shown promising effectiveness. However, these methods are computationally
expensive. Meanwhile, since these priors involved subproblems cannot be solved
explicitly, approximate solution is commonly used, which limits the best
exploitation of their capability. To address these problems, this work firstly
proposes a simplified sparsity prior of local minimal pixels, namely patch-wise
minimal pixels (PMP). The PMP of clear images is much more sparse than that of
blurred ones, and hence is very effective in discriminating between clear and
blurred images. Then, a novel algorithm is designed to efficiently exploit the
sparsity of PMP in deblurring. The new algorithm flexibly imposes sparsity
inducing on the PMP under the MAP framework rather than directly uses the half
quadratic splitting algorithm. By this, it avoids non-rigorous approximation
solution in existing algorithms, while being much more computationally
efficient. Extensive experiments demonstrate that the proposed algorithm can
achieve better practical stability compared with state-of-the-arts. In terms of
deblurring quality, robustness and computational efficiency, the new algorithm
is superior to state-of-the-arts. Code for reproducing the results of the new
method is available at https://github.com/FWen/deblur-pmp.git.
