| Summary: | Motion blur is a common problem in optical imaging, which is caused by the relative displacement between the subject and the camera in the exposure process of the camera. This can result in motion blur of the acquired image, reduce the image resolution and affect the imaging quality. Motion blur image restoration technology uses the existing motion blur image to restore the clear image through the modeling of imaging physical process and mathematical solution without re-photographing the target scene. It has an important application value in the civil and military fields. Solving the problem of motion blur caused by camera jitter and object motion during camera imaging is a very challenging problem. When the popular generative adversarial network model is directly applied to the image blur blind removal task, serious pattern collapse phenomenon will occur. In this paper, we propose a novel motion image deblurring model based on pyramid attention mechanism-oriented symmetry generative adversarial network. This new method does not need to predict the fuzzy kernel of the blurred images, and can directly realize the blind removal of image motion blur. Based on the original CycleGan, the network structure and loss function of the symmetry generative adversarial network are improved. The accuracy of blind removal of motion images is improved, and the stability of the network is greatly enhanced in the case of limited samples. The generative network adopts the encoding and decoding structure, and introduces the feature pyramid attention mechanism. The combination of multi-scale pyramid features and attention mechanism can capture more rich advanced features to improve the model performance. In the experiment, the RMSProp algorithm is used to optimize the network training. Finally, a clear image is obtained through network adversarial training between generative and discriminant network. Experimental results on the related image blur benchmark datasets show that the restoration quality of the proposed method is higher in terms of subjective and objective evaluation. Meanwhile, the restoration results can achieve better results in subsequent object detection tasks.
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