Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use HWnet-predicted weights in WLRMF, with HWnet trained on spatial-spectral variant Gaussian noise | WLRMF (HW-LRMF) · ICVL, Cases 2–7 | PSNR: 32.52 vs 32.43; 33.67 vs 22.11; 31.80 vs 29.13; 31.58 vs 28.91; 31.26 vs 28.08; 28.38 vs 25.38 | Helped |
| Use HWnet-predicted weights in WLRMF | WLRMF (HW-LRMF) · ICVL, Case 1 (i.i.d Gaussian) | PSNR: 34.93 vs 37.09 | Hurt |
| Apply HWnet-predicted weights | NAILRMA · ICVL, Case 1 | PSNR: 32.47 -> 36.52 | Helped |
| Apply HWnet-predicted weights | NGmeet · ICVL, Case 2 | PSNR: 34.93 -> 37.18 | Helped |
| Apply HWnet-predicted weights | LLRT · ICVL, Case 3 | PSNR: 38.42 -> 39.24 | Helped |
| Use the weighted loss from HWnet instead of ordinary noisy-image MSE | DHP · ICVL, Case 2 (non i.i.d Gaussian) | PSNR | Mixed |
| Use the weighted loss from HWnet instead of ordinary noisy-image MSE | DHP · ICVL, Case 5 (Gaussian + Impulse) | PSNR | Helped |
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