Hyperspectral images (HSIs) are unavoidably contaminated by different types of noise during data acquisition and transmission, e.g., Gaussian noise, impulse noise, stripes, and deadlines. A variety of mixed noise reduction approaches are developed for HSI, in which the subspace-based methods have achieved comparable performance. In this paper, a novel subspace-based nonlocal low-rank and sparse factorization (SNLRSF) method is proposed to remove the mixture of several types of noise. The SNLRSF method explores spectral low rank based on the fact that spectral signatures of pixels lie in a low-dimensional subspace and employs the nonlocal low-rank factorization to take the spatial nonlocal self-similarity into consideration. At the same time, the successive singular value decomposition (SVD) low-rank factorization algorithm is used to estimate three-dimensional (3-D) tensor generated by nonlocal similar 3-D patches. Moreover, the well-known augmented Lagrangian method is adopted to solve final denoising model efficiently. The experimental results over simulated and real datasets demonstrate that the proposed approach outperforms the related state-of-the-art methods in terms of visual quality and quantitative evaluation.

Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples’ heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstrate our work on building, training, and serving such out-of-the-box cloud deep learning service for cardiac disease detection from ECG named CardioLearn. The analytic ability of any other ECG recording devices can be enhanced by connecting to the Internet and invoke our open API. As a practical example, we also design a portable smart hardware device along with an interactive mobile program, which can collect ECG and detect potential cardiac diseases anytime and anywhere.