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Improved Imaging by Invex Regularizers with Global Optima Guarantees

NeurIPS 2022 · Optimization › Optimizers · approach: Surrogate losses and robust solvers · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace l1-norm regularizer with invex Eq. (6) (p=0.5)FISTA-style Algorithm 1 · Deconvolution CS, noiselessPSNR: 29.97 -> 33.40Helped
replace l1-norm with invex Eq. (6) at 20 dBAlgorithm 1 · Deconvolution CS, SNR 20 dBPSNR: 21.80 -> 24.60Helped
replace l1-norm with invex Eq. (6) at 30 dBAlgorithm 1 · Deconvolution CS, SNR 30 dBPSNR: 24.91 -> 27.61Helped
use invex Eq. (9) instead of Eq. (6)Algorithm 1 · Deconvolution CS, noiselessPSNR: 33.40 -> 30.00Hurt
invex Eq. (6) vs nonconvex SCADAlgorithm 1 · Deconvolution CS, noiselessPSNR: 30.55 -> 33.40Helped
invex Eq. (6) vs nonconvex MCPAlgorithm 1 · Deconvolution CS, noiselessPSNR: 31.30 -> 33.40Helped
invex Eq. (6) vs ReconNet (non-iterative CNN)Algorithm 1 · Deconvolution CS, noiselessPSNR: 27.01 -> 33.40Helped
add plug-and-play Noise2Void prior with invex Eq. (6) (p=0.8) replacing l1-normAlgorithm 3 · Plug-and-play deconvolution, noiselessPSNR: 31.10 -> 34.51Helped
plug-and-play with Eq. (6) vs l1-norm at 20 dBAlgorithm 3 · Plug-and-play deconvolution, SNR 20 dBPSNR: 22.95 -> 25.55Helped
unrolled LISTA with Eq. (6) (p=0.85) instead of l1-normAlgorithm 2 · CS noiseless, m/n=0.2PSNR: 27.95 -> 31.32Helped
increase measurement ratio m/n from 0.2 to 0.6 with Eq. (6)unfolded LISTA · CS noiselessPSNR: 31.32 -> 41.27Helped
unrolled ISTA-Net with Eq. (6) instead of l1-normAlgorithm 2 · CS noiseless, m/n=0.6PSNR: 38.02 -> 43.61Helped

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