Image and Graph Restoration Dependent on Generative Adversarial Network Algorithm

As a research hotspot in the field of deep learning, image inpainting is of great significance in people\u27s real life. There are various problems in the existing image inpainting algorithms, resulting in the visual inability to meet people\u27s requirements. In view of the defects of the existing image inpainting algorithms, such as low accuracy, poor visual consistency and unstable training, in this paper the missing content is generated by adjusting the available data. For a data set, first analyze the samples in the data set into sample points in the probability distribution, quickly generate a large number of forged images by using the generation countermeasure network, search the code of the closest damaged image, and then infer the missing content through the generation model. Combining the semantic loss function and perceptual loss function, the problem that the gradient is easy to disappear is solved. Experiments show that the algorithm improves the accuracy of image restoration, can generate more realistic repaired images, is suitable for the repair of various types of images, and realizes the realism of photos

Minimizing L1 over L2 norms on the gradient

In this paper, we study the L1/L2 minimization on the gradient for imaging applications. Several recent works have demonstrated that L1/L2 is better than the L1 norm when approximating the L0 norm to promote sparsity. Consequently, we postulate that applying L1/L2 on the gradient is better than the classic total variation (the L1 norm on the gradient) to enforce the sparsity of the image gradient. To verify our hypothesis, we consider a constrained formulation to reveal empirical evidence on the superiority of L1/L2 over L1 when recovering piecewise constant signals from low-frequency measurements. Numerically, we design a specific splitting scheme, under which we can prove subsequential and global convergence for the alternating direction method of multipliers (ADMM) under certain conditions. Experimentally, we demonstrate visible improvements of L1/L2 over L1 and other nonconvex regularizations for image recovery from low-frequency measurements and two medical applications of MRI and CT reconstruction. All the numerical results show the efficiency of our proposed approach.Comment: 26 page