Sanechips Technology Co., Ltd.

Zte Corporation

Sz Dji Technology Co., Ltd.

Shenzhen Dapu Microelectronics Co., Ltd.

Dapustor Corporation

Bin Han

Xiao Zhang

Ke Xu

Dehui Kong

Hong Wang

Yafei Yang

Guoning Lu

Xin Liu

Hao Wang

Jianhua Zhang

Hong Yang Wang

Ning Wang

Sheng Luo

Fang Zhu

Bo Cheng

Wenjun Zhao

Long Huang

Hongbo Wan

Degen Zhen

Zhou Han

Yue Dong

Chong Xiang

Shuai Jiao

Shaoquan Liu

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.

Image processing method and device, computer storage medium and terminal

A data recovery method, an apparatus, and a solid state drive are provided. The method includes: copying, after an abnormality occurs in the solid state drive, memory data of a first memory space to a solidified area of a second memory space, and then writing address information of the solidified area to a third memory space; recovering, after a controller is reset for the first time, the memory data of the first memory space according to the address information of the solidified area; flushing data of the second memory space into a flash memory space according to the recovered memory data of the first memory space; and performing, after the controller is reset for the second time, data recovery on the solid state drive according to memory data of the flash memory space.

Data recovery method, apparatus, and solid state drive

A data recovery method for a storage medium, a data recovery system, and a related device are provided. The method includes: when a write failure occurs during writing data into the storage medium, extracting original data corresponding to the data failed to be written and original verification data in an original RAID stripe to which the original data belongs; obtaining new verification data by performing calculation on the original data and the original verification data by using a preset verification algorithm; forming a new RAID stripe by using valid data other than the original data in the original RAID stripe and the new verification data, and recording data relations in the new RAID stripe; and when a reading failure occurs during reading the data in the new RAID stripe, recovering, according to the data relations in the new RAID stripe, the data failed to be read.

Data recovery method in storage medium, data recovery system, and related device

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.

Image reconstruction method and device, apparatus, and non-transitory computer-readable storage medium

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.

Virtual reality display method, device and apparatus, and computer storage medium

An electronic device includes a slice splitter and a slice encoder. The slide splitter is configured to split video data into a plurality of slices. Each slice contains a plurality of data blocks and each data block contains a plurality of data points. The slice encoder includes one or more video encoding circuits and is configured to encode the data blocks in a plurality of data streams concurrently to obtain encoded data streams and combine the encoded data streams into a combined data stream.

Parallel video encoding

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Shenzhen, China

Bin Han