In image acquisition

the relative displacement between the imaging device and shooting scene causes loss of information and blurring degradation

which significantly affects image quality and visual experience and results in various complicated processing on the images.Non-blind deconvolution obtains a sharp latent image from a blurred image with a known blur kernel.By contrast

blind deconvolution

which aims to estimate the unknown blur from the observed blurred image and recover a sharp original image

is challenging.Thus

blind deconvolution has been an active research area in image processing communities over the last four decades.Given the problem of image deblurring

most approaches introduce an image prior that favors natural images over degraded ones.This approach can achieve high-quality results.Thus

a blind deblurring approach based on strong edges is proposed in this study. The sparsity of image gradient is combined with the strong edges of the latent image gradient and then regularized by an adaptive l-norm.The adaptive l-norm is a weighted metric that measures the usefulness of gradients

the large metric corresponding to pixels in flat regions or rich-texture regions

and the small metric corresponding to pixels in strong-edged regions.For the sparsity and the continuity of the blur kernel

the pixels and the gradient of the blur kernel are regularized using the l and l norms

respectively.Meanwhile

the blur kernel is normalized in advanced and introduced into the optimization model as a regularized term

where strong edges are used to direct the blur kernel estimate.The prior sparse gradient image and the compound priors for sparsity of the gradient of the blur kernel

the continuity and the normalization of the blur kernel are considered；thus the proposed model favors sharp images over the degraded images.Obtaining an accurate solution using the proposed model is slightly complicated.Thus

an alternating iteration approach is performed to solve the model by updating the process iteration through two easy sub-problems

namely

latent image estimate and blur kernel estimate.An augmented Lagrangian method (ALM) is used to identify the latent image

and a quadratic function method is used to determine the blur kernel.For the latent image solution

the sub-problem is equivalently formulated as an unconstrained optimization problem by introducing an auxiliary variable in the sub-model

which is performed using ALM.The solution is obtained by an alternating optimization strategy

such that is updated by iteratively hard thresholding method

and latent image is updated by iteratively fast Fourier transform in the frequency domain.In the blur kernel process

the sub-problem is equivalently described as an unconstrained problem

with auxiliary variable .To obtain the solution

the sub-problem is decomposed into two easy sub-problems with regard to and the blur kernel and iterating each alternately

such that is updated by the iteratively hard thresholding method

and the blur kernel is updated by the iteratively quadratic function optimization.Image deconvolution using a prior hyper-Laplacian can obtain a clear image with main structures and few artifacts but sometimes it fails to preserve some fine details.Moreover

total variation norm can preserve abundant small textures but retains noise and ringing artifacts.For the restored image estimation

the estimated blur kernel and the two algorithms are combined to utilize their merits

reduce their limitations

and build the corresponding optimization with respect to the intermediate with rich saliency edges which are the blurred observation enhanced by a shock filter

and the sharp image is then obtained by averaging the results recovered from the prior hyper-Laplacian-based method and the total variation norm-based method.Thus

the ringing effect in the restored image is reduced while preserving more image details. To test the effectiveness of the proposed algorithm

the Levin set and the actual blurred images are tested and compared with state-of-the-art algorithms.The ratio of deconvolution error (RDE) and peak signal-to-noise ratio (PSNR) are used to evaluate the results in the Levin set.Experiments on the Levin data set show that the proposed method achieves a successful rate of blind deconvolution (100%) even with the smallest RDE of less than 2.6.This result is higher than that of the second-best method (0.3) and much higher than that of the worst method (2.4).The largest PSNR is 30.59 dB

which is greater than those of the second-best approach (1.01 dB) and the worst approach (19.81 dB).The extensive details show that the blur kernel obtained from the proposed method has more accurate support

less noise

and achieves sharp images with better visual effects.Experiments on actual color images demonstrate the proposed method can obtain more accurate blur kernel and better image quality compared with the state-of-the-art algorithms.The proposed method provides a dominant recovery but is also time consuming compared with the state-of-the-art algorithms. The proposed method outperforms the other algorithms and appears to be outstanding in latent image and blur kernel estimate and in image quantitative and qualitative motion deblurring.This method can be used in remote sensing

medicine

and other fields.Comparison of time consumption shows that better overall performance of the proposed method can be obtained by improving the algorithm optimization

as well as performing parallel implementation in the future.