Abstract: The present application provides an image processing method, an image processing device, a computer storage medium and a terminal, the image processing method includes: determining convolution kernels of at least two sizes for feature extraction; performing sparsity constraint for the determined convolution kernels of at least two sizes for feature extraction through a preset objective function; and performing feature extraction on an image based on the convolution kernels subjected to the sparsity constraint.

Abstract: Provided in the embodiments of the present disclosure are a method and apparatus for training an image reconstruction model, a storage medium, and an electronic device. The method includes: acquiring a target teacher image reconstruction model; training, by using the target sample image set, a student image reconstruction model to be trained, and ending the training until a target loss value satisfies a second preset loss condition, so as to obtain a target student image reconstruction model, wherein the target loss value is a loss value determined according to a first loss value and a second loss value, and the second loss value is a loss value determined according to a difference value between a predicted value and a real value respectively determined by the student image reconstruction model to be trained and the target teacher image reconstruction model.

Abstract: The present application provides a video processing method, a device, and a storage medium. The method includes: coding and decoding an original video to obtain a mixed resolution video, where the mixed resolution video includes a first resolution frame and a second resolution frame each corresponding to a key frame, and a third resolution frame corresponding to a non-key frame, where the first resolution frame has a resolution higher than a resolution of the second resolution frame or a resolution of the third resolution frame; and amplifying, according to the first resolution frame and the second resolution frame, the third resolution frame corresponding to the non-key frame to output an amplified video, where the amplified video includes the first resolution frame corresponding to the key frame, and an amplified target frame corresponding to the non-key frame.

Abstract: Systems, devices, and methods for dermal treatments are provided. Various systems, devices, and methods provide treatment options with a handheld device, intradermal or subdermal fluid delivery via a needle or microneedle. For fluid delivery, system can include an injector, fluid-filled container, and a needle or hollowed microneedle. A fluid-filled container can be compatibly coupled with a treatment device such to perform the various dermal treatments. Further, fluid delivery systems can be utilized in a number of applications, including medications and supplements for the skin.

Abstract: There are provided a vector operation method, a vector operator, an electronic device, and a computer-readable storage medium. The vector operation method includes: splitting a target vector operation to be performed to determine a plurality of basic operations in a predetermined execution order; sequentially generating, according to the predetermined execution order, a plurality of basic operation instructions corresponding to the plurality of basic operations; and sequentially executing, according to the predetermined execution order, the plurality of basic operation instructions on initial data to be subjected to the target vector operation, so as to implement the target vector operation on the initial data, wherein in two adjacent basic operations, to-be-calculated data for a latter basic operation is an operation result of a former basic operation.

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: 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: Systems, devices, and methods for intradermal or subdermal fluid delivery via a microneedle are provided. A delivery system can include an injector, fluid-filled cartridge or syringe, and a needle. A cartridge or syringe can be compatibly coupled with an injector device such that the injector device is capable of ejecting fluid out of the cartridge and though the needle. Fluid delivery systems can be utilized in a number of applications, including medications and supplements for the skin.

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 thermoplastic composite material has an inner layer material and at least one layer of outer layer material. The inner layer material is a core layer that contains fiber bundles, a first thermoplastic resin and a first auxiliary agent; and the at least one layer of outer layer material wraps the core layer and is a resin layer comprising a second thermoplastic resin and an optional second auxiliary agent. The fiber bundles extend continuously from one end of the core layer to the opposite end thereof. The inner layer-outer layer composite structure, can effectively improves the processing performance of the thermoplastic composite material and the lubricity between fibers and resin matrixes during injection molding, and improves the fluidity of the fibers in a resin matrix melt.

Abstract: An image reconstruction method, device and apparatus and non-transitory computer-readable storage medium are disclosed. The method may include: determining norms of convolution kernels of each convolutional layer of a deep neural network model; determining the convolution kernels with norms greater than or equal to a preset threshold in each convolutional layer to obtain a target convolution kernel set of each convolutional layer; processing an input image of each convolutional layer by using the convolution kernels in the target convolution kernel set of each convolutional layer respectively, to obtain a first image processing result; obtaining a second image processing result by performing interpolation on an initial image; and determining a fusion result according to the first image processing result and the second image processing result and reconstructing the initial image according to the fusion result.

Abstract: Dictionary learning method and means for zero-shot recognition can establish the alignment between visual space and semantic space at category layer and image level, so as to realize high-precision zero-shot image recognition. The dictionary learning method includes the following steps: (1) training a cross domain dictionary of a category layer based on a cross domain dictionary learning method; (2) generating semantic attributes of an image based on the cross domain dictionary of the category layer learned in step (1); (3) training a cross domain dictionary of the image layer based on the image semantic attributes generated in step (2); (4) completing a recognition task of invisible category images based on the cross domain dictionary of the image layer learned in step (3).

Abstract: The present application provides a video processing method, a device, and a storage medium. The method includes: coding and decoding an original video to obtain a mixed resolution video, where the mixed resolution video includes a first resolution frame and a second resolution frame each corresponding to a key frame, and a third resolution frame corresponding to a non-key frame, where the first resolution frame has a resolution higher than a resolution of the second resolution frame or a resolution of the third resolution frame; and amplifying, according to the first resolution frame and the second resolution frame, the third resolution frame corresponding to the non-key frame to output an amplified video, where the amplified video includes the first resolution frame corresponding to the key frame, and an amplified target frame corresponding to the non-key frame.

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: Dictionary learning method and means for zero-shot recognition can establish the alignment between visual space and semantic space at category layer and image level, so as to realize high-precision zero-shot image recognition. The dictionary learning method includes the following steps: (1) training a cross domain dictionary of a category layer based on a cross domain dictionary learning method; (2) generating semantic attributes of an image based on the cross domain dictionary of the category layer learned in step (1); (3) training a cross domain dictionary of the image layer based on the image semantic attributes generated in step (2); (4) completing a recognition task of invisible category images based on the cross domain dictionary of the image layer learned in step (3).

Abstract: A de-interlacing processing method, a de-interlacing processing device and a computer-readable storage medium are provided. The method includes acquiring image content characteristic information of a pixel point to be interpolated; and determining according to the image content characteristic information whether a de-interlacing algorithm based on motion adaptive or a de-interlacing algorithm based on motion compensation is adopted to perform de-interlacing processing.

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: A virtual reality display method, device, apparatus and storage medium are provided. The method includes: acquiring multimedia data to be displayed and a visible region of a viewer on a curved display surface, wherein the visible region is obtained by projecting a visible range of the viewer to the curved display surface, and is not larger than a display area of the curved display surface; determining target curvatures of at least two positions in the visible region of the viewer, wherein in the target curvatures, target curvatures of different positions are related to a distance to a center of the visible region of the viewer; adjusting, based on the target curvatures of the at least two positions in the visible region, a curvature of a corresponding position on the curved display surface; and mapping the multimedia data to be displayed to the curved display surface having the adjusted curvature.