scCO2-Brine-Glass Dataset for Comparing Image Denoising Algorithms

Analyses of this sample.

• (Traditional Filter) Non-Local Means - Denoised
• (Traditional Filter) Gaussian - Denoised
• (Traditional Filter) Median - Denoised
• (Traditional Filter) Anisotropic Diffusion - Denoised
• (Traditional Filter) Bilateral - Denoised
• (Traditional Filter) Symmetric Nearest Neighbor - Denoised
• (Supervised DL Model) Noise2Clean - Denoised
• (Supervised DL Model) Cycle Consistent Generative Adversarial Network - Denoised
• (Supervised DL Model) Residual Dense Network - Denoised
• (Unsupervised DL Model) Noise2Void - Denoised
• (Semi-supervised DL Model) Noise2Noise (50%) - Denoised
• (Unsupervised DL Model) Noise2Noise - Denoised
• (Semi-supervised DL Model) Noise2Noise (75%) - Denoised
• (Semi-supervised DL Model) Noise2Noise (25%) - Denoised
• HQ (Segmented)
• LQ (Segmented)
• Non-Local Means (Segmented)
• Gaussian (Segmented)
• Median (Segmented)
• Anisotropic Diffusion (Segmented)
• Bilateral (Segmented)
• Symmetric Nearest Neighbor (Segmented)
• Noise2Clean (Segmented)
• Cycle Consistent Generative Adversarial Network (Segmented)
• Residual Dense Network (Segmented)
• Noise2Void (Segmented)
• Noise2Noise (50%) (Segmented)
• Noise2Noise (Segmented)
• Noise2Noise (75%) (Segmented)
• Noise2Noise (25%) (Segmented)