Research map / Learning theory and algorithms / Matrix and tensor methods
Tensor Compressive Sensing Fused Low-Rankness and Local-Smoothness
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fuse low-rankness and local-smoothness with parameter-free tensor correlated total variation | Tensor compressive sensing · HYDICE Washington DC Mall, SR=1% | PSNR: 20.820 -> 25.800 | Helped |
| Fuse low-rankness and local-smoothness with parameter-free tensor correlated total variation | Tensor compressive sensing · HYDICE Washington DC Mall, SR=1% | SSIM: 0.5695 -> 0.7640 | Helped |
| Fuse low-rankness and local-smoothness with parameter-free tensor correlated total variation | Tensor compressive sensing · HYDICE Washington DC Mall, SR=1% | ERGAS: 334.43 -> 190.99 | Helped |
| Fuse low-rankness and local-smoothness with parameter-free tensor correlated total variation | Tensor compressive sensing · HYDICE Urbanpart, SR=1% | PSNR: 23.628 -> 26.585 | Helped |
| Fuse low-rankness and local-smoothness with parameter-free tensor correlated total variation | Tensor compressive sensing · HYDICE Urbanpart, SR=1% | SSIM: 0.6762 -> 0.7472 | Helped |
| Fuse low-rankness and local-smoothness with parameter-free tensor correlated total variation | Tensor compressive sensing · HYDICE Urbanpart, SR=1% | ERGAS: 236.05 -> 166.44 | Helped |
| Use DFT rather than DCT for the transform (ablation) | TCTV · HSI restoration at lower and higher sampling ratios | PSNR: 22.195 -> 21.906; 41.088 -> 41.629 | Mixed |
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