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Deeply Aggregated Alternating Minimization for Image Restoration

CVPR 2017 · Image generation › Image restoration · approach: Priors for restoration networks · 33 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use three AM iterations rather than one or two (ablation)DeepAM · BSD68 denoising, σ=15, 25, and 50PSNR: 31.40 -> 31.68; 31.65 -> 31.68; 28.95 -> 29.21; 29.18 -> 29.21; 25.94 -> 26.24; 26.20 -> 26.24Helped
Replace point-wise proximal mapping with learned deep aggregationDeepAM · Single-image denoisingPSNR: +0.3~0.5dBHelped
Use DeepAM(3) instead of the strongest listed baselineBSD68 denoising · σ=15, 25, and 50PSNR: 31.42 -> 31.68; 28.91 -> 29.21; 26.00 -> 26.24Helped
Use DeepAM(2) instead of the strongest listed baselineDepth super-resolution · NYU v2 and Middlebury, ×4, ×8, and ×16BMP (δ=3): 0.68 -> 0.57; 2.41 -> 3.14; 1.92 -> 1.58; 5.97 -> 5.78; 5.82 -> 4.63; 12.18 -> 10.45Mixed
Use DeepAM(2) instead of the strongest listed baselineRGB/NIR restoration · Five-pair average, σ=50PSNR: 28.28 -> 29.28Helped
Concatenate guidance halfway through feature extraction rather than at the input (ablation)Joint-restoration DeepAM · Joint restorationrestoration effectivenessHelped

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