Abstract: Disclosed is a fusion network-based method for image super-resolution and non-uniform motion deblurring. The method achieves, for the first time, restoration of a low-resolution non-uniform motion-blurred image based on a deep neural network. The network uses two branch modules to respectively extract features for image super-resolution and non-uniform motion deblurring, and achieves, by means of a feature fusion module that is trainable, adaptive fusion of outputs of the two branch modules for extracting features. Finally, an upsampling reconstruction module achieves a non-uniform motion deblurring and super-resolution task. According to the method, a self-generated set of training data is configured to perform offline training on a network, thereby achieving restoration of the low-resolution non-uniform motion-blurred image.

Abstract: The present disclosure relates to the technical field of data anomaly detection and alarm, and more particularly, to a data anomaly statistics and alarm method and apparatus, and an electronic device, which effectively relieve the problem of alarm missing in the related technologies, thereby effectively avoiding the loss of enterprises due to alarm missing, the method includes: acquiring a detection time and a detection result of each of a plurality of data respectively through detecting; counting the number of target data in a current time window, and generating an alarm signal to prompt in response to the number of target data obtained by the counting being greater than a preset number threshold corresponding to a quality level of the data; moving the current time window backward according to a stepping duration included in an obtained stepping duration setting rule corresponding to the quality level of the data, and the current time window moved backward is used as a new current time window.

Abstract: Disclosed is a fusion network-based method for image super-resolution and non-uniform motion deblurring. The method achieves, for the first time, restoration of a low-resolution non-uniform motion-blurred image based on a deep neural network. The network uses two branch modules to respectively extract features for image super-resolution and non-uniform motion deblurring, and achieves, by means of a feature fusion module that is trainable, adaptive fusion of outputs of the two branch modules for extracting features. Finally, an upsampling reconstruction module achieves a non-uniform motion deblurring and super-resolution task. According to the method, a self-generated set of training data is configured to perform offline training on a network, thereby achieving restoration of the low-resolution non-uniform motion-blurred image.