Coloring Algorithm With Generative Adversial Network

With the continuous development of artificial intelligence,the trend of combining computer technology with traditional industries is obviously.In recent years,with the development of digital media technology,many methods for coloring grayscale images have been proposed.In the past,methods of image colorization were based on color transfer and expansion,requiring manual intervention,and the coloring effect was unsatisfactory.Therefore,the colorization research based on deeplearning method has important significance and broad application prospects.In recent years,the Generative Adversial Networks(GANs)have outperforms in the fields of image generation,image denoising,image style conversion,etc.,which fully proves the potential of GANs in image processing.Therefore,this paper uses the GANs to coloring images.The main contents are as follows.(1)With image graying preprocessing in some public data sets such as Imagenet,CelebA,etc.Paired feature maps are obtained to train the neural network.(2)The existing networks are difficult to extract global features on high-resolution images.Therefore Non-local Blocks are added to improve the global feature extraction effect.Then two loss functions are added to make the generated images more realistic.(3)Exploring the influence of the color characteristics of the latent space and noise information on the generated images.The result is inspiring.(4)In order to solve the problem that it is difficult to train against the network,the progressive growth training method and the two time scale update rules are used for training.At the same time,the spectrum normalization is used to increase the training stability and training effect of the model,and finally a more realistic image can be generated..We verify our network coloring effects and quantify the coloring effects through various evaluation indicators such as Inception Score.Experiments prove that our network performs better on the high-resolution images,and can generate high-quality color pictures with vivid colors and clear resolution.