Abstract: An image processing method and device, and a computer-readable storage medium are disclosed. The method includes: performing a channel expansion process on the input image to obtain a first intermediate image; performing a channel decomposition process for multiple times based on the first intermediate image, wherein each time of channel decomposition process includes: decomposing an image to be processed into a first decomposition image and a second decomposition image; concatenating first decomposition images generated in each time of channel decomposition process and second decomposition image generated in the last time of channel decomposition process to obtain a concatenated image; performing a post-processing process on the concatenated image to obtain a second intermediate image; and fusing the second intermediate image with the input image to obtain the first output image.

Abstract: A method for reconstructing an HDR image includes: acquiring a plurality of original images with same photographing scene and different exposure degrees; screening out a reference image from the plurality of original images, performing feature alignment processing on remaining original images according to the reference image, and obtaining displacement images of the remaining original images; determining an enhanced image according to the reference image and the displacement images of the remaining original images, wherein the enhanced image is obtained by performing image enhancement processing on a fused image after a down-sampling operation, and the fused image is obtained by performing feature fusion on the reference image and the displacement images of the remaining original images; and reconstructing the HDR images corresponding to the plurality of original images according to the enhanced images.

Abstract: A container taking and conveying assembly includes: a base; a bearing assembly configured to bear a container and defining an accommodating space for accommodating the container; a taking and placing assembly configured to take out the container from a first target position and load the container onto the bearing assembly, or unload the container from the bearing assembly and place the container at a second target position; and a moving assembly configured to drive the taking and placing assembly to move in a first movement trajectory where the moving assembly is configured to drive the taking and placing assembly to move in the accommodating space to load or unload the container, and a second movement trajectory where the moving assembly is configured to drive the taking and placing assembly to leave the accommodating space to allow the container to enter the accommodating space and be borne on the bearing assembly.

Abstract: A video frame interpolation processing method and apparatus thereof, and a non-transitory readable storage medium. The method includes: (S101) acquiring a target video; (S102) acquiring a first frame number set, based on the target video; (S103) acquiring at least one speech fragment of a target audio; (S104) acquiring a second frame number set, based on the at least one speech fragment of the target audio and the target video; (S105) performing frame interpolation on the target video, based on the first frame number set and the second frame number set. The target video includes a plurality of video frames, each of the plurality of video frames has a corresponding frame number, and frame numbers of the plurality of video frames increase progressively in time order. The first frame number set includes at least one first frame number, and each of the at least one first frame number corresponds to a video frame with picture switch. The target audio corresponds to the target video in time domain.

Abstract: A method and an apparatus for video frame interpolation processing, and a non-instantaneous readable storage medium. The method for video frame interpolation processing, including: acquiring the first video frame and the second video frame of the video; acquiring the first comparison result between the first video frame and the second video frame based on the first video frame and the second video frame; and determining whether to interpolate a frame between the first video frame and the second video frame based on the first comparison result. The first video frame and the second video frame are adjacent temporally, and the first video frame is a forward frame of the second video frame. The first comparison result indicates whether a picture switch exists between the first video frame and the second video frame.

Abstract: A method for training an image processing model includes: inputting a low dynamic range image into a first initial image processing model, so that a high dynamic range reconstruction processing is performed on the low dynamic range image to generate a first high dynamic range image; inputting the low dynamic range image into a second initial image processing model to generate a first coefficient; generating a second high dynamic range image according to the first high dynamic range image and the first coefficient; generating a loss function according to data pairs of the second high dynamic range image and a real high dynamic range image, the real high dynamic range image corresponding to the low dynamic range image; and training the first initial image processing model and the second initial image processing model with the loss function.

Abstract: An image processing method, an apparatus, an electronic device and a non-transient computer-readable storage medium. The image processing method includes: (S11) acquiring an original image; (S12) performing a fuzzy processing to the original image to obtain a fuzzy image; (S13) performing a high-dynamic-range image to the original image by using a first network model obtained by pre-training, to obtain a first characteristic matrix, wherein the first network model includes a dense residual module and a gate-control-channel conversion module; (S14) obtaining an auxiliary characteristic matrix of the original image according to the fuzzy image, wherein the auxiliary characteristic matrix includes detail information of the original image and/or low-frequency information of the original image; (S15) obtaining a target image according to the first characteristic matrix and the auxiliary characteristic matrix.

Abstract: An image processing method and device, and a computer-readable storage medium are disclosed. The method includes: performing a channel expansion process on the input image to obtain a first intermediate image; performing a channel decomposition process for multiple times based on the first intermediate image, wherein each time of channel decomposition process includes: decomposing an image to be processed into a first decomposition image and a second decomposition image; concatenating first decomposition images generated in each time of channel decomposition process and second decomposition image generated in the last time of channel decomposition process to obtain a concatenated image; performing a post-processing process on the concatenated image to obtain a second intermediate image; and fusing the second intermediate image with the input image to obtain the first output image.

Abstract: Techniques are described for a Hypertext Transfer Protocol (HTTP) request monitoring application that provides an automated solution for managing cross-region compliance of incoming HTTP requests received by a web application or service. An HTTP request monitoring application comprises a plurality of modular components that can be readily integrated into a request processing pipeline of a web server or, more generally, into any application that processes HTTP requests. The components can include, for example, one or more analysis components, used to analyze various data elements contained in HTTP requests to estimate an origin requests, and one or more action components, used to perform various actions responsive to analysis components identifying potentially non-compliant requests (e.g., where such actions may include blocking the incoming requests, logging information about the requests, generating notifications, sending metrics to a monitoring service, etc.).