Image Deblurring Algorithm Based on Generative Adversarial Network

To improve the quality of blurred images captured under haze conditions, we propose a convolutional network defogging algorithm based on the generative adversarial learning. The convolutional network jointly optimizes the dielectric transmission map and the atmospheric light value, which avoids the defect of separation optimization affecting the overall defogging performance. To generate more vivid and clear images, we introduce a new loss function to optimize the network. The loss function includes L₂ loss and bidirectional gradient loss, feature loss, and discriminator loss. It guarantees good defogging performance from four aspects. In addition, in the network training process, we use real image and synthetic foggy images as datasets at the same time. In the synthesis process, we process the synthetic images using the guided filtering algorithm. Finally, we introduce several evaluation indexes to verify the effect of the method. The experimental data and results under different methods show that the method makes significant improvements on the basis of the existing methods.