  Image quality is the basis of image communication and understanding tasks.
Due to the blur and noise effects caused by imaging, transmission and other
processes, the image quality is degraded. Blind image restoration is widely
used to improve image quality, where the main goal is to faithfully estimate
the blur kernel and the latent sharp image. In this study, based on
experimental observation and research, an adaptively sparse regularized
minimization method is originally proposed. The high-order gradients combine
with low-order ones to form a hybrid regularization term, and an adaptive
operator derived from the image entropy is introduced to maintain a good
convergence. Extensive experiments were conducted on different blur kernels and
images. Compared with existing state-of-the-art blind deblurring methods, our
method demonstrates superiority on the recovery accuracy.
