图像拍摄过程中会不可避免地产生由相机抖动或物体运动引发的图像模糊问题。针对该问题,提出了一种基于图像边缘判别机制的盲图像去模糊方法,以恢复图像并使之具有清晰的边缘。首先,提出一个PNet子网,将模糊图像作为输入并利用数据驱动的方式进行判别学习,直到网络收敛。将模糊图像再次输入到训练收敛的PNet子网的生成器中,可得到去模糊图像,并将此图像记作边缘弱化图像。其次,提出一个DNet子网,将模糊图像和边缘弱化图像输入到DNet子网中进行训练,得到的训练收敛的DNet生成器即为图像去模糊模型。此外,提出边缘重建函数和图像语义内容损失函数用于约束图像的边缘和语义信息。最后,提出图像边缘判别的目标损失函数,使得DNet子网的判别器在完成生成图像与标签图像真假判别的同时,还完成对边缘弱化图像和标签图像的进一步判别,因此图像边缘信息的判别学习得到了强化。实验结果表明,所提方法能够有效地恢复大幅度模糊图像和运动引起的模糊图像,这证明了边缘判别机制对图像边缘恢复的重要作用。

In the process of image acquisition, the image blurring problem is always inevitably caused by camera shaking or object movement. In order to solve this problem, a blind image deblurring method based on image edge determination mechanism is proposed to restore images with sharp edges. First, a PNet subnet is proposed to set blurry images as inputs, and determination learning is carried out by using a data driven method until the network is converged. The blurring image is input again to the generator of training converge in the PNet subnet, which can obtain deblurring images and the deblurring images are noted as edge-weakened images. Second, a DNet subnet is proposed, both blurry images and edge-weakened images are served as inputs for training, and the DNet generator of training convergence is image deblurring model. In addition, the edge reconstruction function and image semantic content loss function are proposed to constrain the image edge and sematic information. Finally, an object loss function for image edge determination is proposed to make the DNet subnet generator complete the true-false determination of generated images and labeled images and finish the further determination of edge-weakened images and labeled images. Therefore, the determination learning of image edge information is enhanced. Experimental results show that the proposed method can restore large-scale blurring images and blurring images caused by movement, which proves the important role of edge determination mechanism in the image edge restoring.