Blind Image Inpainting Using Pyramid GAN on Thyroid Ultrasound Images Thyroid ultrasound image is an important basis for artificial intelligence assisted treatment of thyroidrelated 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(PyGAN) for blind image inpainting to remove cross symbols. PyGAN 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 noncompletion area. The inpainting results of the proposed PyGAN 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 pixellevel realism. Both qualitative and quantitative comparisons are superior to existing learningnonlearning image inpainting works.

Superresolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks Abstract   Background and objective  Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, highresolution MRI scans is time consuming and peak signaltonoise ratio (PSNR) is low. The existing superresolution methods attempt to resolve this issue, but there are still shortcomings, such as hallucinate details after superresolution, low precision after reconstruction, etc. To dispose these problems, we propose the Laplacian Pyramid Generation Adversarial Network (LSRGAN) in order to generate visually better cardiovascular ultrasound images so as to aid physician diagnosis and treatment.    Methods and results  In order to address the problem of low image resolution, we used the Laplacian Pyramid to analyze the highfrequency detail features of superresolution (SR) reconstruction of images with different pixel sizes. To eliminate gradient disappearance, we implemented the least squares loss function as the discriminator, we introduce the residualdense block (RDB) as the basic network building unit is used to generate higher quality images. The experimental results show that the LSRGAN can effectively avoid the illusion details after superresolution and has the best reconstruction quality. Compared with the stateoftheart methods, our proposed algorithm generates higher quality superresolution images that comes with higher peak signaltonoise ratio and structural similarity (SSIM) scores.    Conclusion  We implemented a novel LSRGAN network model, which solves reduces insufficient resolution and hallucinate details of MRI after superresolution. Our research presents a superior superresolution method for medical experts to diagnose and treat myocardial ischemia and myocardial infarction.