Objective

2

Motion blur blind restoration is the process of restoring a clear image without knowledge of the motion blur kernel function of the image. Resolving the fuzzy kernel and the clear image accurately and efficiently is the key to blind restoration. The regularization constraint technique approximates the reconstructed image to the ideal image by properly adding regular items with prior knowledge in the model. The blind restoration problem can be solved quickly and effectively using this technique. Unlike the single regularization method

the hybrid property regularization method can use known multiple prior conditions to impose numerous constraints on the model. While improving the accuracy of model solving

the number of iterations is reduced. Therefore

the multiple mixed feature regularization constraint method for the motion blur blind restoration method should be studied. To address the problems of poor anti-noise performance

incomplete fuzzy kernel smoothness constraint

and edge blurring of restored images in existing methods

a mixed feature regularization constraint method for the motion blur blind restoration method is proposed.

Method

2

First

to improve the accuracy of the estimated fuzzy kernel model

the edge of the image is extracted to act on the fidelity term and thus estimate the fuzzy kernel. Edge extraction is easily disturbed by noise

thereby leading to the generation of false edges or additional noise and consequently reducing the estimation accuracy of the fuzzy kernel model. Therefore

the Local weighted total variation structure extraction algorithm is used to detect the significant edges of the blurred image to improve the anti-noise performance of the fuzzy kernel model. Then

sparse regular items and improved smooth regular items are added in this work to improve the sparsity and smoothness of the fuzzy kernel. L

0

norm smoothness regularization is complicated; thus

the L

1

norm of the fuzzy kernel is used to achieve the sparse constraint. An improved multiple mixed regularization term is used to achieve the smoothing restraint while further suppressing the outlier value due to the insignificant suppression effect of the Tikhonov regularization term outlier value. Finally

for the restored image to have a heavy-tailed character and to enhance the sharpness of the edge of the restored image

the super Laplacian prior and edge-preserving regularization terms are added in this work. The Laplacian prior is used to fit the gradient distribution of the clear image to enrich the edge details due to the poor fitting effect of other prior distributions. The edge resolution of the restored image is poor because only the heavy-tailed property constraint is used on the restored image model. Therefore

edge preserving regularization items are added to make the edge of the restored image close to the sharpened edge of the clear image.

Result

2

In this study

the advantages of the improved model and the proposed algorithm are verified by two groups of experiments. In the first experiment

the simulated motion blur image is taken as the experimental object. A comparison and an analysis of the restoration effects of the five combined steps verifies the robustness of the improved fuzzy kernel model and the improved restored image model. Experimental results show that the edge details of the improved model restoration image in this work are clear and natural and the evaluation index is noticeably improved. In the second experiment

the real motion blur image of a small UAV is used as the experimental object. The robustness and practicability of the proposed algorithm are analyzed. Moreover

the proposed algorithm is compared with the traditional algorithm. Experimental results show that the standard deviation of the restored image increases by approximately 11.4%. The average gradient increases by approximately 30.1%. The information entropy increases by approximately 2.2%. Furthermore

our method has better subjective visual effects than the traditional method has.

Conclusion

2

For the blind restoration of motion blur images

the superiority of the improved model proposed in this work is demonstrated through theoretical analysis and experimental verification. Restoration through the proposed algorithm is better than that with the traditional algorithm.