Adversarial Image Colorization Method Based on Semantic Optimization and Edge Preservation

As a medium for transmitting visual information, image is a direct reflection of the objective existence of the natural world. Grayscale images lack more visual information than color images. Therefore, it is of great significance to study the colorization of grayscale images. At present, the problems of semantic ambiguity, boundary overflow and lack of color saturation exist in both traditional and deep learning methods. To solve the above problems, an adversarial image colorization method based on semantic optimization and edge preservation is proposed. By improving generative and discriminative networks and designing loss functions, deeper semantic information and sharper edges of images can be learned by our network. Our experiments are carried out on the public datasets Place365 and ImageNet. The experimental results show that the method in this paper can reduce the color anomaly caused by semantic ambiguity, suppress the color blooming in the image boundary area and improve the saturation of the image. Our work achieves competitive results on objective indicators of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and learned perceptual image patch similarity (LPIPS), with values of 30.903 dB, 0.956 and 0.147 on Place365 and 30.545 dB, 0.946 and 0.150 on ImageNet, which proves that this method can effectively colorize grayscale images.