To address the issues of blurred contours and lost details of portrait image with motion blur after restoration， a moving portrait deblurring method based on multi-level jump residual group Generation Adversarial Network （GAN） was proposed. Firstly， the residual block was improved to construct the multi-level jump residual group module， and the structure of PatchGAN was also improved to make GAN better combine with the image features of each layer. Secondly， the multi-loss fusion method was adopted to optimize the network to enhance the real texture of the reconstructed image. Finally， the end-to-end mode was used to perform blind deblurring on the motion blurred portrait image and output clear portrait image. Experimental results on CelebA dataset show that the Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） of the proposed method are at least 0.46 dB and 0.05 higher than those of the Convolutional Neural Network （CNN）-based methods such as DeblurGAN （Deblur GAN）， Scale-Recurrent Network （SRN） and MSRAN （Multi-Scale Recurrent Attention Network）. At the same time， the proposed method has fewer model parameters， faster restoration， and more texture details in the restored portrait images.

The manual labeling of abundant data is laborious and the amount of Magnetic Resonance Imaging （MRI） data from a single imaging center is limited. Concerning the above problems， a Magnetic Resonance Semi-Supervised Learning （MRSSL） method utilizing multi-imaging center labeled and unlabeled MRI data was proposed and applied to knee abnormality classification. Firstly， data augmentation was used to provide the inductive bias required by the model . Next， the classification loss and the consistency loss were combined to constraint an artificial neural network to extract the discriminative features from the data. Then， the features were used for the MRI knee abnormality classification. Additionally， the corresponding Magnetic Resonance Supervised Learning （MRSL） method only using labeled samples was proposed and compared with MRSSL for the same labeled samples. The results demonstrate that MRSSL surpasses MRSL in both model classification performance and model generalization ability. Finally， MRSSL was compared with other semi-supervised learning methods. The results indicate that data augmentation plays an important role on performance improvement， and with stronger inclusiveness for MRI data， MRSSL outperforms others on the knee abnormality classification.