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Combining the Power of Internal & External Denoising

Combining the Power of Internal & External Denoising. Inbar Mosseri The Weizmann Institute of Science , ISRAEL ICCP , 2013. Outline. Introduction Background P atch_psnr R esults. Internal Denoising. Denoising using other noisy patches within the same noisy image. NLM BM3D.

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Combining the Power of Internal & External Denoising

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  1. Combining the Power of Internal & External Denoising Inbar Mosseri The Weizmann Institute of Science , ISRAEL ICCP , 2013

  2. Outline • Introduction • Background • Patch_psnr • Results

  3. Internal Denoising Denoising using other noisy patches within the same noisy image • NLM • BM3D

  4. External Denoising Denoising using external clean natural patches or a compact representation • EPLL • Sparse

  5. Internal vs. External Denoising a) Original b) Noisy input c) Internal NLM d) External NLM e) Combinining (c)&(d)

  6. Internal vs. External Patch Preference the higher the noise in the image, the stronger the preference for internal denoising

  7. PatchSNR

  8. PatchSNR patches with low PatchSNR (e.g., in smooth image regions) tend to prefer Internal denoising patches with high PatchSNR (edges, texture) tend to prefer External denoising

  9. Overfitting the Noise-Mean the empirical mean/variance of the noise within an individual small patch is usually not zero/σ2 d : patch size ~ N(0,)

  10. Fitting ofthe Noise Mean The denoising error grows linearly with the deviation from zero of the empirical noise-mean within the patch. In contrast, the denoising error is independent of the empirical noise variance within the patch.

  11. Overfit the Noise Detail For smooth patches with low var(p), after removing the mean , pn = p + n . These patches are dominated by noise. There are high correlation between a random noise patch n and its similar natural patch NN(n)

  12. Overfit the Noise Detail For smooth patches with low var(p), after removing the mean , pn = p + n . These patches are dominated by noise. There are high correlation between a random noise patch n and its similar natural patch NN(n)

  13. Estimate the PatchSNR But var(n) is also unknown and patch-dependent. we approximate var(n) using one of the existing denoising algorithm and get the denoised version of , so：

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