Abstract: A cooking device includes a pot lid assembly and a pot body. The pot lid assembly includes a pot lid including a plurality of first pot teeth. The pot lid including at least two first fool-proof structures each formed at one of the plurality of first pot teeth. The pot body includes a plurality of second pot teeth. The pot body includes at least two second fool-proof structures each provided at one of the plurality of second pot teeth. The pot lid and the pot body are configured to be buckled with each other via a one-to-one cooperation between the at least two first fool-proof structures and the at least two second fool-proof structures.

Abstract: A method performed by an electronic device, may include: acquiring image brightness information of a first image and scene brightness information of a shooting scene corresponding to the first image; and performing brightness adjustment on the first image based on the image brightness information and the scene brightness information.

Abstract: The present disclosure provides a method performed by an electronic device, an electronic device, a storage medium and a program product, relating to the field of computer vision, image processing and artificial intelligence. The method includes: extracting at least one object center area and at least one object boundary area from a first image; and aligning the extracted object center area with the object boundary area to obtain an object segmentation result of the first image.

Abstract: A method performed by an electronic device and an electronic device are provided. The method includes acquiring a first image, determining restoration style information of the first image, and restoring the first image based on the restoration style information by using an artificial intelligence (AI) network to obtain a restored image.

Abstract: An example embodiment may involve receiving, at a node of a radio access network, input signals on a plurality of wireless channels, wherein uplink channels in the plurality of wireless channels are respectively associated with downlink channels in the plurality of wireless channels; determining up-link characteristics for the uplink channels; providing, to a trained neural network, the uplink characteristics, wherein the trained neural network was iteratively trained, with a training data set of recorded uplink characteristics and recorded downlink characteristics, to form predictions of the recorded downlink characteristics from the recorded uplink characteristics, wherein the trained neural network includes an encoder that maps the recorded uplink characteristics to latent vector representations, and a decoder that maps the latent vector representations to the predictions; receiving, from the trained neural network, a prediction of downlink characteristics for the downlink channels; and transmitting, on th

Abstract: An image processing method comprises obtaining an input image; converting the input image or a feature map of the input image into a plurality of target input images or target feature maps, wherein a resolution of each of the target input images or the target feature maps is smaller than a resolution of the feature map of the input image or the input image, and pixels at the same position in each of the target input images or the target feature maps are of a neighborhood relationship with the input image or the feature map of the input image; processing at least a part of the plurality of target input images or target feature maps by one or more convolution blocks in a convolutional neural network; and increasing a resolution of a feature map output from the one or more convolution blocks in the convolutional neural network.

Abstract: The disclosure provides an image optimization method, system, and storage medium. The image optimization method includes extracting texture quality information from an input image. The texture quality information indicates a spatial distribution of texture quality in the input image. The image optimization method also includes performing, according to the texture quality information, texture restoration on a set region in the input image to generate a texture restored image.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium, and the image processing method includes: acquiring an input image; detecting a target area in the input image; and processing the target area, wherein the processing of the target area includes: obtaining a feature map of the target area, rearranging feature blocks in the feature map in a feature space, and obtaining an output image after the target area is processed based on the rearranged feature blocks and the feature map.

Abstract: Provided is an image processing method to process an image, the method including acquiring a plurality of channel images of the image to be processed, acquiring a noise intensity corresponding to the each of the plurality of channel images of the image to be processed, denoising the image to be processed based on the noise intensity corresponding to the each of the plurality of channel images of the image, and obtaining the denoised image.

Abstract: The disclosure provides an image optimization method, system, and storage medium. The image optimization method includes extracting texture quality information from an input image. The texture quality information indicates a spatial distribution of texture quality in the input image. The image optimization method also includes performing, according to the texture quality information, texture restoration on a set region in the input image to generate a texture restored image.

Abstract: An image processing method comprises obtaining an input image; converting the input image or a feature map of the input image into a plurality of target input images or target feature maps, wherein a resolution of each of the target input images or the target feature maps is smaller than a resolution of the feature map of the input image or the input image, and pixels at the same position in each of the target input images or the target feature maps are of a neighborhood relationship with the input image or the feature map of the input image; processing at least a part of the plurality of target input images or target feature maps by one or more convolution blocks in a convolutional neural network; and increasing a resolution of a feature map output from the one or more convolution blocks in the convolutional neural network.