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: 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: Provided are method and device for inverse quantization and inverse transformation and non-transitory computer-readable storage medium.

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.

Abstract: A data processing method and device, a storage medium and an electronic device are disclosed. The method includes: reading M*N feature map data of all input channels and weights of a preset number of output channels, here a value of M*N and a value of the preset number are respectively determined by preset Y*Y weights; inputting the read feature map data and the weights of the preset number of output channels into a multiply-add array of the preset number of output channels for a convolution calculation; here a mode of the convolution calculation includes: not performing the convolution calculation in a case that the feature map data or the weights of the output channels are zero, and selecting one from same values for the convolution calculation in a case that there are a plurality of feature map data with the same values; and outputting a result of the convolution calculation.

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: 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: 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.

Abstract: Provided are method and device for inverse quantization and inverse transformation and non-transitory computer-readable storage medium.