The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.

Objective: The sliding motion of the liver during respiration violates the homogeneous motion smoothness assumption in conventional non-rigid image registration and commonly results in compromised registration accuracy. This paper presents a novel approach, registration with 3D active contour motion segmentation (RAMS), to improve registration accuracy with discontinuityaware motion regularization. Methods: A Markov random field-based discrete optimization with dense displacement sampling and self-similarity context metric is used for registration, while a graph cuts-based 3D active contour approach is applied to segment the sliding interface. In the first registration pass, a maskfree L1 regularization on an image-derived minimum spanning tree is performed to allow motion discontinuity. Based on the motion field estimates, a coarse segmentation finds the motion boundaries. Next, based on MR signal intensity, a fine segmentation aligns the motion boundaries with anatomical boundaries. In the second registration pass, smoothness constraints across the

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