Image Processing and Optimization Using Deep Learning-Based Generative Adversarial Networks (GANs)
Yang Zhang, Hangyu Xie, Shikai Zhuang, Xiaoan Zhan
Abstract
This paper introduces the application of generative adversarial networks (GANs) in image processing and optimization. GANs model can generate realistic images by co-training generator and discriminator, and achieve remarkable results in image restoration tasks. CATGAN and DCGAN are two commonly used GAN models applied to image classification and image restoration respectively. In addition, the global and local image patching methods can effectively fill the missing areas in the image and show good results in the restoration of large images. In conclusion, the image processing and optimization method based on GANs has shown great potential in practice and provides beneficial insight for future research and application in the field of image processing.