Generative adversarial network for dental image super-resolution, image sharpening, and denoising

Abstract

A novel GAN is trained to predict high fidelity synthetic images based on low quality input dental images. The GAN further takes input anatomic masks as inputs with each image, the masks labeling pixels of the image corresponding to dental features. The GAN includes an encoder-decoder generator with semantically aware normalization between stages of the decoder according to the masks. The predicted synthetic dental image and an unpaired dental image are evaluated by a first discriminator of the GAN to obtain a realism estimate. The synthetic image and an unpaired dental image may be processed using a pretrained dental encoder to obtain a perceptual loss. The GAN is trained with the realism estimate, perceptual loss, and L1 loss. Utilization may include inputting noisy, low contrast, low resolution, blurry, or degraded dental images and outputting high resolution, denoised, high contrast, deobfuscated, and sharp dental images.