Low-rank tensor completion method for alpha-order total variation constraint of damaged video
A fully variable and broken technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as blurred edges of structural fine-grained texture areas, repair images, loss of affine details, etc., and achieve the effect of overcoming the oscillation phenomenon
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[0137] The following will use YUV video data to illustrate the present invention's low-rank tensor completion method for the α-order total variation constraints of damaged video to further illustrate its effect:
[0138] The experimental data comes from YUV video sequences, and the video data are suzie and hall_qcif respectively. The experimental video data is read into MATLAB, some commonly used 4:2:0 YUV format video test sequences are used, and the first 100 frames are selected as the experimental data, so the data size is 176×144×100, they can be regarded as a 3D tensor. By randomly masking a part of the original tensor data in all channels of the experimental video data, the remaining pixels are used to form a damaged 3D tensor to complete the tensor Among them, the data loss rate of the experimental video is 95% and 75%. Simultaneously convert the three-dimensional tensor Expand along each module into a two-dimensional expansion matrix Here N=3, the sizes of the...
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