Reynolds MacKenzie (stoolvelvet15)

The problem is solved by the alternating direction of multipliers (ADMM) and linearized approximation, respectively, to improve the computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm is effective to enhance the ISAR image, robust to noise, and more impressively, very efficient to implement.Hand pose understanding is essential to applications such as human computer interaction and augmented reality. Recently, deep learning based methods achieve great progress in this problem. However, the lack of high-quality and large-scale dataset prevents the further improvement of hand pose related tasks such as 2D/3D hand pose from color and depth from color. In this paper, we develop a large-scale and high-quality synthetic dataset, PBRHand. The dataset contains millions of photo-realistic rendered hand images and various ground truths including pose, semantic segmentation, and depth. Based on the dataset, we firstly investigate the effect of rendering methods and used databases on the performance of three hand pose related tasks 2D/3D hand pose from color, depth from color and 3D hand pose from depth. This study provides insights that photo-realistic rendering dataset is worthy of synthesizing and shows that our new dataset can improve the performance of the state-of-the-art on these tasks. This synthetic data also enables us to explore multi-task learning, while it is expensive to have all the ground truth available on real data. Evaluations show that our approach can achieve state-of-the-art or competitive performance on several public datasets.Fluorescence molecular tomography (FMT) is a promising and high sensitivity imaging modality that can reconstruct the three-dimensional (3D) distribution of interior fluorescent sources. However, the spatial resolution of FMT has encountered an insurmountable bottleneck and cannot be substantially improved, due to the simplified forward model and the severely ill-posed inverse problem. In this work, a 3D fusion dual-sampling convolutional neural network, namely UHR-DeepFMT, was proposed to achieve ultra-high spatial resolution reconstruction of FMT. Under this framework, the UHR-DeepFMT does not need to explicitly solve the FMT forward and inverse problems. Instead, it directly establishes an end-to-end mapping model to reconstruct the fluorescent sources, which can enormously eliminate the modeling errors. Besides, a novel fusion mechanism that integrates the dual-sampling strategy and the squeeze-and-excitation (SE) module is introduced into the skip connection of UHR-DeepFMT, which can significantly improve the spatial resolution by greatly alleviating the ill-posedness of the inverse problem. To evaluate the performance of UHR-DeepFMT network model, numerical simulations, physical phantom and in vivo experiments were conducted. The results demonstrated that the proposed UHR-DeepFMT can outperform the cutting-edge methods and achieve ultra-high spatial resolution reconstruction of FMT with the powerful ability to distinguish adjacent targets with a minimal edge-to-edge distance (EED) of 0.5 mm. ISO1 It is assumed that this research is a significant improvement for FMT in terms of spatial resolution and overall imaging quality, which could promote the precise diagnosis and preclinical application of small animals in the future.Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation