For satellite brightness temperature images, researchers are constantly pursuing higher resolutions to obtain more detailed meteorological information. In this paper, a novel deep-learning-based modelling approach, named Zero-Sample Gradient Guidance Spatial Resolution Enhancement (ZSGRE), is developed explicitly for microwave radiometers. The detailed model, including mathematical derivation and key parameters, is presented. Subsequently, the proposed approach is applied in four scenarios: synthetic scene, simulated geographical brightness temperature, practical measurement of microwave radiometer in Fengyun-3D (FY-3D), and a cyclone analysis on the Atlantic. Compared with other methods, the proposed ZSGRE method improves 2.51% of SSIM (structural similarity), enhances 2.3 dB of PSNR (Peak Signal-to-Noise Ratio), and decreases 15.8% of IFOV (Instantaneous Field of View). Such applications demonstrate ZSGRE’s significant performance: zero-sample preparation and spatial resolution enhancement.

Cervical vertebrae age estimation empowers clinicians to determine the development status of children and adolescents for precise orthodontic diagnosis and treatment. This work proposes a fully automated cervical vertebrae maturation staging framework that uses deeply learned CT image segmentation and classification. Specifically, such a two-step framework first employs convolutional neural networks (i.e., nnU-Net) to precisely segment the cervical vertebrae bones. Then, we propose parallel regression-classification networks for the bone staging using the segmented results. Specifically, the regression path introduces prior knowledge (i.e., bone anatomical parameters) to supervise the classification path. We evaluate our method on two clinical CT databases (60 balance volumes and 85 imbalance volumes), with the experimental results showing that our method attains higher classification accuracy than state-of-the-art methods. In particular, it can improve the accuracy from (0.5833, 0.5294) to (0.6667, 0.6471) on (balance, imbalance) data, respectively. Besides, our method also achieves an average accuracy of (0.65, 0.6353) for the 5-fold cross-validation.

Accurate segmentation of the cervical vertebrae in CT images can assist clinicians in analyzing the adolescent patient’s growth and development and establishing an effective orthodontic plan. This work develops an enhanced U2-Net architecture for fully automatic cervical vertebrae segmentation in CT images. Specifically, such an enhanced architecture first creates a deepwise separable residual U-shape module (DUM) in different levels and a convolutional attention module embedded into a U-structure for encoding and decoding and obtains a coarse segmentation. Then, it reuses a DUM to refine the segmentation. We evaluated our method on 60 CT scans, with the experimental results showing that our method attains much better segmentation performance than state-of-the-art network models. Particularly, it can improve the dices similarity coefficient, intersection over union, precision, and recall from (0.9586, 0.9207, 0.9584, 0.9591) to (0.9755, 0.9524, 0.9832, 0.9708), respectively, while it can reduce the model parameters from 44.0M to 16.5M.

Selecting targets directly in the virtual world is difficult due to the lack of haptic feedback and inaccurate estimation of egocentric distances. Proprioception, the sense of self-movement and body position, can be utilized to improve virtual target selection by placing targets on or around one's body. However, its effective scope is limited closely around one's body. We explore the concept of virtually-extended proprioception by appending virtual body parts mimicking real body parts to users' avatars, to provide spatial reference to virtual targets. Our studies suggest that our approach facilitates more efficient target selection in VR as compared to no reference or using an everyday object as reference. Besides, by cultivating users' sense of ownership on the appended virtual body part, we can further enhance target selection performance. The effects of transparency and granularity of the virtual body part on target selection performance are also discussed.