Blind Image Inpainting Using Pyramid GAN on Thyroid Ultrasound Images

Thyroid ultrasound image is an important basis for artificial intelligence assisted treatment of thyroid-related diseases, but existing images usually contain special cross symbols which represent the location of nodules marked by doctors, thus affecting the features and diagnostic results extracted by the deep learning algorithm. We propose Pyramid GAN(Py-GAN) for blind image inpainting to remove cross symbols. Py-GAN contains a generator with pyramid structure and a global discriminator. The global discriminator improves the authenticity of the corrupted regions and image consistency. The generator uses the joint context loss to get clear image restoration, which prevents the information loss of non-completion area. The inpainting results of the proposed Py-GAN not only maintains the texture and structural information of the original image, but also has the greatest advantage that there are no artifacts in the corrupted regions, achieving pixel-level realism. Both qualitative and quantitative comparisons are superior to existing learning/non-learning image inpainting works.