Blind Image Deblurring Based on Image Edge Determination Mechanism

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 arc noted as edge-weakened images. Second, a DNet subnet is proposed, both blurry images and edge-weakened images arc 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 arc proposed to constrain the image edge and somatic 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.