Abstract: Disclosed are a method and apparatus for training an image restoration model, an electronic device, and a computer-readable storage medium. The method for training an image restoration model includes: pre-processing training images to obtain a low-illumination image sample set (110); determining, based on low-illumination image samples in the low-illumination image sample set and the image restoration model, a weight coefficient of the image restoration model (120), wherein the image restoration model is a neural network model determined on a U-Net network and a deep residual network; and adjusting the image restoration model according to the weight coefficient, and further training the adjusted image restoration model using the low-illumination image samples until the image restoration model restores parameters of all the low-illumination image samples in the low-illumination image sample set into a preset range (130).

Abstract: The present disclosure provides a method for training an image enhancement model, the image enhancement model includes an enhancement module including convolution branches corresponding to brightness intervals; and the method includes: inputting a sample image to the image enhancement model, and acquire a result image output by the image enhancement model; calculating losses including an image loss of the result image relative to a Ground Truth image, and a first constraint loss of brightness histogram constraint of each of the convolution branches of an image output from each of the convolution branches relative to the Ground Truth image; adjusting the enhancement module according to the losses; and in a case where a training end condition is not met, returning to the operation of inputting the sample image to the image enhancement model. The present disclosure further provides an image enhancement method and a computer-readable medium.

Abstract: Dynamic adjusting of pod resource limits is provided at runtime of a container orchestration platform pod. The process includes deploying a container orchestration platform pod with one or more pod resources in a computing environment, where the pod resources have one or more associated pod resource limits. Further, the process includes monitoring a runtime resource usage of the container orchestration platform pod, and predicting, by a trained machine learning model, upcoming resource usage of the container orchestration platform pod. The predicting uses, at least in part, the monitored runtime resource usage. Further, the process includes dynamically adjusting a pod resource limit of the one or more pod resource limits of the container orchestration platform pod in the computing environment. The dynamically adjusting is based on the monitored runtime resource usage, and the predicted upcoming resource usage.

Abstract: The present disclosure discloses a video denoising method and device, and a computer-readable storage medium. The video denoising method includes: dividing each video frame in an input video frame sequence into sub-image blocks, and calculating a block variance of each of the sub-image blocks; calculating an average variance of the sub-image blocks in a current video frame according to the calculated block variances, determining a noise level of the current video frame according to the average variance, selecting a denoise strength and a noise characteristic curve that match the noise level; and denoising the current video frame according to the denoise strength and the noise characteristic curve.

Abstract: Provided are a method and apparatus for presenting image for a VR device, a device and a non-transitory computer-readable storage medium. The method for presenting image for a VR device includes steps of: acquiring input image data; establishing a correspondence between the input image data and output image data in a polar coordinate system, the polar coordinate system having a pole which is an intersection of a center of a field of view of the VR device and a display plane; obtaining image data to be presented according to the output image data; and presenting the image data to be presented on the basis of a convex lens.

Abstract: According to an aspect of the disclosure, the disclosure is directed to a wireless communication apparatus on a vehicle. The wireless communication the apparatus includes (not limited to): first wireless transceiver configured to transmit and receive data on a first communication path; a second wireless transceiver configured to transmit and receive data on a second communication path; and a processor electrically connected to the first wireless transceiver and the second wireless transceiver and configured at least to: establish, as a default mean of communication, multiple communication paths; transmit by the first wireless transceiver, as the default mean of communication, a first data packet to the network located outside of the vehicle on a first communication path; and transmit by the second wireless transceiver, as the default mean of communication, a first duplicated data packet of the first data packet to the network on a second communication path.

Abstract: The invention provides a self-modulating input electrical power laser control system and method. After the laser is turned on for a period of time, the control module reduces the initial electrical power to the operating power of the laser, and it maintains the operating power until the laser is turned off, which can reduce the extra power consumption and achieve the energy-efficiency.

Abstract: The present application relates to the field of image processing, and provides an image denoising method and apparatus, an electronic device and a storage medium. The image denoising method includes: acquiring an image to be processed, and inputting the image to be processed into an image denoising model to acquire a denoised image, wherein the image denoising model is a model formed by combining a U-shaped network, a residual network and a dense network.

Abstract: A low-illuminance video processing method, a low-illuminance video processing device and a storage medium are disclosed. The method includes: acquiring a same number of preceding frame images and subsequent frame images corresponding to a current video frame of a low-illuminance video to obtain a frame image set corresponding to the current video frame, and performing traversal on the low-illuminance video to obtain frame image sets corresponding to all video frames; after performing image alignment on all frame images in the frame image sets corresponding to all video frames, inputting the frame image sets into a pre-trained low-illuminance image enhancement model to obtain enhanced frame images; and generating an enhanced video based on the enhanced frame images.

Abstract: Disclosed are a method and apparatus for training an image restoration model, an electronic device, and a computer-readable storage medium. The method for training an image restoration model includes: pre-processing training images to obtain a low-illumination image sample set (110); determining, based on low-illumination image samples in the low-illumination image sample set and the image restoration model, a weight coefficient of the image restoration model (120), wherein the image restoration model is a neural network model determined on a U-Net network and a deep residual network; and adjusting the image restoration model according to the weight coefficient, and further training the adjusted image restoration model using the low-illumination image samples until the image restoration model restores parameters of all the low-illumination image samples in the low-illumination image sample set into a preset range (130).

Abstract: The present disclosure discloses a video denoising method and device, and a computer-readable storage medium. The video denoising method includes: dividing each video frame in an input video frame sequence into sub-image blocks, and calculating a block variance of each of the sub-image blocks; calculating an average variance of the sub-image blocks in a current video frame according to the calculated block variances, determining a noise level of the current video frame according to the average variance, selecting a denoise strength and a noise characteristic curve that match the noise level; and denoising the current video frame according to the denoise strength and the noise characteristic curve.

Abstract: Provided in the embodiments of the present application are an online video data output method, and a system, related to the technical field of the internet. The online video data output method is applicable in the cloud platform and comprises: receiving real-time information transmitted by a client and processing data on the basis of the real-time information to produce image information, the real-time information being transmitted from a server to the client for the client to forward to the cloud platform; acquiring rendering data from the image information and rendering on the basis of the rendering data to produce a rendered video; processing the rendered video to produce video data and outputting the video data.

Abstract: A method for processing live broadcast data, a system, an electronic device, and a storage medium, relating to the technical field of Internet live broadcast. A live broadcast providing terminal sends to a server a data execution instruction and graphical interaction data corresponding to the data execution instruction, such that the server performs processing on the graphical interaction data in response to the data execution instruction, and delivers an obtained graphical interaction scene. In this way, by configuring a graphical interaction data processing procedure on a server side of a live broadcast system, the data processing amount of the live broadcast providing terminal can be reduced, thereby reducing hardware overheads of the live broadcast providing terminal and improving a live broadcast effect.

Abstract: Provided are a method and apparatus for presenting image for a VR device, a device and a non-transitory computer-readable storage medium. The method for presenting image for a VR device includes steps of: acquiring input image data; establishing a correspondence between the input image data and output image data in a polar coordinate system, the polar coordinate system having a pole which is an intersection of a center of a field of view of the VR device and a display plane; obtaining image data to be presented according to the output image data; and presenting the image data to be presented on the basis of a convex lens.

Abstract: A disclosed feedback method for a smart wearable device includes the following steps: acquiring characterized information of a current motion; searching the database to determine whether corresponding characterized information of motion is found in a database based on the acquired characterized information; prompting to notify that the user's motion is correct, recording the acquired characterized information to have a segmented data, and inserting the segmented data into to the database if the corresponding characterized information is found; and prompting to notify that the user's motion is incorrect if the corresponding characterized information is not found. A motion detector implementing the method and a smart wearable device are also provided.

Abstract: A method, computer system, and a computer program product for migrating a service from one container to another container is provided. The present invention may include obtaining a first group of requests that are accessing a service launched in a first container instance and a second group of requests that are waiting for accessing the service. The present invention may also include generating a migrated service in a second container instance by migrating the service from the first container instance to the second container instance based on the obtained first and second groups of requests. The present invention may further include directing the second group of requests to the migrated service in the second container instance.

Abstract: A method, computer system, and a computer program product for migrating a service from one container to another container is provided. The present invention may include obtaining a first group of requests that are accessing a service launched in a first container instance and a second group of requests that are waiting for accessing the service. The present invention may also include generating a migrated service in a second container instance by migrating the service from the first container instance to the second container instance based on the obtained first and second groups of requests. The present invention may further include directing the second group of requests to the migrated service in the second container instance.

Abstract: A method, computer system, and a computer program product for migrating a service from one container to another container is provided. The present invention may include obtaining a first group of requests that are accessing a service launched in a first container instance and a second group of requests that are waiting for accessing the service. The present invention may also include generating a migrated service in a second container instance by migrating the service from the first container instance to the second container instance based on the obtained first and second groups of requests. The present invention may further include directing the second group of requests to the migrated service in the second container instance.

Abstract: A method, computer system, and a computer program product for migrating a service from one container to another container is provided. The present invention may include obtaining a first group of requests that are accessing a service launched in a first container instance and a second group of requests that are waiting for accessing the service. The present invention may also include generating a migrated service in a second container instance by migrating the service from the first container instance to the second container instance based on the obtained first and second groups of requests. The present invention may further include directing the second group of requests to the migrated service in the second container instance.

Abstract: A method for detecting overlapped bank notes is provided. The method includes: step 10, collecting original image data of bank notes; step 20, performing security thread positioning detection on the original image data of the bank notes, to obtain security thread detection data; step 40, determining whether the security thread detection data meets a preset security thread reference condition, going to step 60 if the security thread detection data meets the preset security thread reference condition, and going to step 70 if the security thread detection data does not meet the preset security thread reference condition; step 60, obtaining a determination result that the bank notes are not overlapped, and ending the method; and step 70, obtaining a determining result that the bank notes are overlapped, and ending the method.