Abstract: An image super-resolution method, a device, and a storage medium are provided. The method includes: determining a super-resolution requirement including a requirement for converting an image with a first resolution into an image with a second resolution greater than the first resolution; and determining a preset image super-resolution network that meets the super-resolution requirement, and inputting a to-be-performed-super-resolution image with the first resolution into the preset image super-resolution network, to obtain a result image with the second resolution output by the preset image super-resolution network.

Abstract: Provided is a video processing method and system, a video playback system, an apparatus, a device, and a medium in the technical field of video processing, and the method includes: acquiring an input video frame and display parameters of the input video frame when it is displayed, the display parameters including at least one of a video format and a resolution; processing the input video frame into a plurality of output video frames based on the display parameters, each output video frame corresponds to a partial region of the input video frame; and displaying the plurality of output video frames on a plurality of display devices so that the plurality of output video frames are displayed according to the display parameters.

Abstract: A multimedia processing method, a multimedia processing apparatus, and a non-transitory computer-readable storage medium are disclosed. The multimedia processing method includes: acquiring an input multimedia, and performing image quality enhancement processing on the input multimedia to obtain an enhanced multimedia. A resolution corresponding to the enhanced multimedia is higher than a resolution corresponding to the input multimedia.

Abstract: Disclosed are a display device system and a method for adaptively enhancing an image quality. The method includes: acquiring an original image, performing iterative detail enhancement processing on the original image by utilizing at least one filter function containing iteratively-updated filter parameters, in a case that a difference between a quality evaluation score of the input image and a quality evaluation score of the detail-enhanced image of the ith iteration satisfies an iteration condition, obtaining filter parameters of an (i+1)th iteration by updating the filter parameters of the ith iteration, and continuing to perform detail enhancement processing on the input image by utilizing at least one filter function containing the filter parameters of the (i+1)th iteration, until a difference between the quality evaluation score of the input image and a quality evaluation score of a detail-enhanced image of the (i+1)th iteration satisfies the iteration condition.

Abstract: A logo labeling method and device, an update method and system, and a storage medium are provided. The logo labeling method includes: acquiring a standard logo picture including a logo; based on the standard logo picture, determining transparency channel information to obtain a first logo picture; determining a circumscribed rectangle frame of the logo in the first logo picture based on the transparency channel information, and performing cutting processing on the first logo picture according to the circumscribed rectangle frame to obtain a second logo picture; performing data augmentation processing based on the second logo picture to obtain at least one third logo picture; and acquiring a background picture, determining pasting position information of the third logo picture in the background picture, and pasting the third logo picture on the background picture according to the pasting position information to obtain at least one logo labeling picture.

Abstract: A power-supply mode switching circuit and an aerosol generating device are provided. The power-supply mode switching circuit includes an external-power-source power-supply circuit, a protection circuit, and a battery power-supply circuit. A node where the external-power-source power-supply circuit is connected to the protection circuit serves as a power-source input end. A node where the external-power-source power-supply circuit is connected to the battery power-supply circuit serves as a power supply end. The protection circuit is configured to control the battery power-supply circuit to be cut off when the power-source input end is connected to the external power source, to make the external power source supply power to the power supply end. The protection circuit is further configured to control the battery power-supply circuit to be conducted when the power-source input end is not connected to the external power source, to make the battery supply power to the power supply end.

Abstract: A method and an apparatus for video processing, and a non-instantaneous readable storage medium. The method for video processing includes: dividing a plurality of video frames in an initial video into a plurality of video segments; determining, based on one frame of the one or more video frames, a display parameter set of a video segment to which the one frame belongs; adjusting other frames in the video segment based on the display parameter set to acquire an intermediate video segment; performing a high dynamic range conversion on the intermediate video segment to acquire a high dynamic range video segment; generating a high dynamic range video based on the high dynamic range video segment. Each of the plurality of video segments comprises one or more video frames, and the plurality of video frames are consecutive.

Abstract: Provided are a method for enhancing a video resolution enhancement, a computer readable storage medium, and an electronic device. The method includes: obtaining multiple image frames as input data, and obtaining initial data by performing feature extraction on the input data using a first three-dimensional convolutional layer; obtaining first feature data by performing down-sampling on the initial data at a preset multiple; obtaining first reference data by performing a convolution operation on the first feature data using a second three-dimensional convolutional layer to merge the first feature data as one frame; and obtaining first output data by performing up-sampling on the first reference data at the preset multiple.

Abstract: The present disclosure provides a defective-spot detection model training method, a defective-spot detection method and a defective-spot restoration method. The defective-spot detection model training method includes: obtaining a first training data set and a second training data set; generating a transparent layer based on a resolution of the sample detection image; replacing an image in a certain region of the transparent layer based on at least one of the plurality of frames of sample defective-spot images to generate a frame of transparent mask; generating a sample training image with a defective-spot based on the frame of transparent mask and the sample detection image; and processing the sample detection image by using the at least one of the plurality of frames of sample defective-spot images to generate a frame of sample training image.

Abstract: The present disclosure discloses a method, apparatus and system for customer group analysis, and a storage medium. The method includes: obtaining video images of a customer group passing by a display apparatus; recognizing and tracking head images of the customer group in the video images, and determining behavior characteristics of individuals in the customer group; and performing statistical analysis on the behavior characteristics of the customer group corresponding to the display apparatus, so as to update a quantity of individuals corresponding to each behavior characteristic.

Abstract: A computer-implemented image-processing method is provided. The computer-implemented image-processing method includes obtaining a pair of training samples including a training image having a first degree of sharpness and a reference image having a second degree of sharpness, the second degree greater than the first degree, at least portions of the training image and the reference image in a same pair having same contents; inputting the training image to the image-enhancing convolutional neural network to generate a training enhanced image; inputting the training enhanced image into an edge detector; generating, by the edge detector, a plurality of first edge maps; inputting the reference image into the edge detector; generating, by the edge detector, a plurality of second edge maps; and tuning parameters in the image-enhancing convolutional neural network to minimize at least the one or more first losses and a second loss.

Abstract: Provided is a method for processing a video. The method for processing the video can segment an initial video into a plurality of video clips, and calculate color temperatures of the plurality of video clips. For each target video clip in the plurality of video clips, the method can determine a color temperature adjustment coefficient of the target video clip based on a color temperature of the target video clip and a color balance correction coefficient thereof, and adjust a color temperature of at least one video frame in the target video clip by using the color temperature adjustment coefficient.

Abstract: A computer-implemented method is provided. The computer-implemented method includes inputting a low-resolution image and a plurality of high-resolution images into a feature extractor, the low-resolution image and the plurality of high-resolution images including images of a target object; obtaining, by the feature extractor, feature maps of the low-resolution image and the plurality of high-resolution images; comparing similarities between the feature maps of the low-resolution image and the plurality of high-resolution images; obtaining selected feature maps of one or more selected high-resolution images of the plurality of high-resolution images most similar to the low-resolution image; inputting the selected feature maps into a generator to output a repair image; enhancing the low-resolution image using a pre-processing image enhancing process to generate an enhanced image; and morphing the repair image with the enhanced image.

Abstract: An image processing method and an image processing device are provided. The image processing method includes performing at least one of a scratch repairing step, a dead point repairing step, a denoising step and a color cast correcting step on a to-be-processed video frame. According to the embodiments of the present disclosure, it is able to repair a scratch and a dead point, remove a noise and/or correct color cast for a video frame, thereby to improve a display effect of the video frame.

Abstract: The present application discloses an image quality evaluation method and apparatus, a device, and a storage medium. The method includes: acquiring a to-be-evaluated image; and inputting the to-be-evaluated image into an image quality evaluation network to obtain an image quality evaluation result, where the image quality evaluation network is configured to extract a first image feature from an input image, perform a shift operation on the first image feature to acquire one or more second image features, and determine the image quality evaluation result by combining the first image feature and the acquired second image features; a size of each of the second image features is the same as a size of the first image feature, and regions with identical values of features exist in different positions between the first image feature and the second image features.

Abstract: A video enhancement method and apparatus, an electronic device, and a storage medium are described. The method comprises: extracting features from M frames of images, so as to obtain at least one first-scale image feature (S310); for each first-scale image feature, performing N-level down-sampling processing on the first-scale image feature, so as to obtain a second-scale image feature (S320); performing N-level up-sampling processing on the second-scale image feature, so as to obtain a third-scale image feature (S330), wherein the input of ith-level up-sampling processing is an image feature obtained after performing superimposition processing on the output of (N+1?i)th-level down-sampling processing and the output of (i?1)th-level up-sampling processing, and the multiple of jth-level up-sampling is the same as the multiple of (N+1?j)th-level down-sampling; and performing superimposition processing on the third-scale image feature and the first-scale image feature.

Abstract: The present disclosure discloses a user interface system, electronic equipment and an interaction method for picture recognition. The electronic equipment includes a display screen, a memory and a processor, a first interface is displayed on the display screen, and the first interface includes at least one primary function classification tag, a plurality of secondary function classification tags and at least one tertiary function classification tag included in each of the secondary function classification tags; and by selecting a tertiary function classification tag, a second interface can be displayed such that a function effect corresponding to the tertiary function classification tag is experienced on the second interface. The electronic equipment can be used such that different tertiary function classification tags can be selected for experience in the first interface, can provide users with practicality, and has certain tool properties.

Abstract: A method for processing an image includes acquiring an input image, performing down-sampling and feature extraction on the input image by an encoder network to obtain multiple feature maps, and performing up-sampling and feature extraction on the multiple feature maps by a decoder network to obtain a target segmentation image. Processing levels between the encoder network and the decoder network for outputting feature maps with the same resolution are connected with each other. The encoder network and the decoder network each includes one or more dense calculation blocks, and at least one convolution module in any dense computation block includes at least one group of asymmetric convolution kernels.

Abstract: The present disclosure provides an image processing method, a computing system, a device, and a readable storage medium. The image processing method includes: using an inverse tone mapping neural network to process a first image, the inverse tone mapping neural network is configured to expand a dynamic range of the first image and a color gamut range of the first image to obtain a second image that is expanded, the inverse tone mapping neural network includes a mapping network, the mapping network is used to realize the expansion, and the inverse tone mapping neural network further includes an attention network, an input of the mapping network and an input of the attention network are both the first image, the attention network is used to process image contents of the first image to generate correction coefficients, and the correction coefficients are used to correct parameters of the mapping network.

Abstract: The present disclosure provides an image processing method and an image processing apparatus. The image processing method includes: obtaining a to-be-converted SDR image; using a first convolutional network to perform feature analysis on the SDR image, to obtain N weights of the SDR image; where the N weights are respectively configured to characterize proportions of color information of the SDR image to color information characterized in preset N 3D lookup tables, the N 3D lookup tables are configured to characterize color information of different types; obtaining a first 3D lookup table for the SDR image according to the N weights and the N 3D lookup tables; using the first 3D lookup table to adjust the color information of the SDR image to obtain an HDR image; and using a second convolutional neural network to perform refinement correction on the HDR image to obtain an output image.