Abstract: An image super-resolution method includes performing frequency domain transformation on a first image to obtain a spectral feature of the first image, the spectral feature representing a distribution of a grayscale gradient in the first image. The method further includes performing blur kernel prediction based on the spectral feature to obtain a blur kernel of the first image, the blur kernel being a convolution kernel. The method also includes performing super-resolution processing on the first image based on the blur kernel to generate a super-resolved image, a definition of the super-resolved image being higher than a definition of the first image.

Abstract: A face image processing method and apparatus, a face image display method and apparatus, and a device are provided, belonging to the technical field of image processing. The method includes: acquiring a first face image of a person; invoking an age change model to predict a texture difference map of the first face image at a specified age, the texture difference map being used for reflecting a texture difference between a face texture in the first face image and a face texture of a second face image of the person at the specified age; and performing image processing on the first face image based on the texture difference map to obtain the second face image.

Abstract: A method, an apparatus, and a device for image processing and a training method thereof are provided. The training method includes obtaining a sample image set, the sample image set comprising a first number of sample images; constructing an image feature set based on the sample image set, the image feature set comprising an image feature extracted from each of the sample images in the sample image set; obtaining a training image set, the training image set comprising a second number of training images; constructing multiple training image pairs based on the training image set and the image feature set; and training the image processing model based on the multiple training image pairs.

Abstract: Methods, apparatus, systems, and articles of manufacture for monitoring robot health in manufacturing environments are described herein. An example system, to monitor health of a robot in a semiconductor wafer manufacturing facility, includes a sensor coupled to the robot. The sensor is to obtain a vibration signal representative of vibration of the robot. The example system also includes a health monitor extract a feature from the vibration signal, compare the feature to a threshold, and, in response to determining the feature satisfies the threshold, transmit an alert.

Abstract: Methods, apparatus, systems, and articles of manufacture for monitoring robot health in manufacturing environments are described herein. An example system, to monitor health of a robot in a semiconductor wafer manufacturing facility, includes a sensor coupled to the robot. The sensor is to obtain a vibration signal representative of vibration of the robot. The example system also includes a health monitor extract a feature from the vibration signal, compare the feature to a threshold, and, in response to determining the feature satisfies the threshold, transmit an alert.