Algorithm Comparison and Evaluation of GAN Models Based on Image Transferring from Desert to Green Field - Zhenyu Liu; Hongjun Li (Advances in Multimedia)
Some time-consuming and labor-intensive techniques, like manual drawing or interactive modeling with an image editing system, are often used to show how a desert area might look after being transformed into a green field (oasis) in an image way. In order to improve the rendering efficiency of image style transformation and increase the variety of renderings, we can build an algorithm for automatically generating style images based on machine learning. In this paper, after comparing seven generative adversarial network (GAN) models in the way of theory analysis, we propose a method for generating green fields using desert images as input data, and a comprehensive comparison is presented on how GANs are currently applied to solve the desert-to-oasis problem. Experimental results show that two GAN models, geometrically consistent GAN and cyclic consistent GAN, have the best transfer effect of a desert image to oasis one in the view of quantitative indicators, Fréchet inception distance, and learned perceptual image patch similarity.