qhjqhj00 image-denoising-eval
Evolutionary Variational Optimization of Generative Models — Drefs et al. (2020) (arXiv:2012.12294, 2020)
What this evaluates
Evaluates the ability of generative models with discrete latents to restore clean images from noisy inputs using a zero-shot, patch-based variational optimization framework.
Datasets
- Standard denoising benchmarks (e.g., House image) — total ?; splits: test (-1)
Metrics
PSNR(primary) — range: dB- Peak Signal-to-Noise Ratio, calculated as 10 * log10(MAX_I^2 / MSE), where MAX_I is the maximum possible pixel value (typically 255 for 8-bit images) and MSE is the mean squared error between the ground-truth clean image and the reconstructed image.
Input / output format
Input: Noisy image patches extracted via a sliding window from a single corrupted image. Patches are processed individually without external training data or known noise levels.
Output: Reconstructed clean image pixels, obtained by taking expectations over the posterior predictive distribution for each patch and aggregating overlapping pixel estimates via weighted averaging.
Scoring recipe
def compute_psnr(clean_img, reconstructed_img):
mse = np.mean((clean_img - reconstructed_img) ** 2)
if mse == 0:
return float('inf')
max_pixel = 255.0 # Assuming 8-bit images
psnr = 10 * np.log10((max_pixel ** 2) / mse)
return psnr
Common pitfalls
- The variational lower bound (learning objective) is not perfectly correlated with PSNR at convergence; the run with the highest bound may not yield the highest PSNR.
- Algorithms require varying amounts of prior knowledge (e.g., noise level, clean training data, specific missing patterns), making direct comparisons difficult without strict categorization.
- Patch-based reconstruction requires careful handling of overlapping regions; incorrect weighting or aggregation of pixel estimates will distort the final PSNR.
Evidence (verbatim from paper)
Finally, we were interested in how well EBSC and ES3C could denoise a given image in terms of the standard peak-signal-to-noise ratio (PSNR) evaluation measure. ... While the PSNR measure requires access to the clean target image, the learning objective can be evaluated without such ground-truth knowledge.
Citation
@misc{drefs2020evolutionary,
title={Evolutionary Variational Optimization of Generative Models},
author={Drefs et al. (2020)},
year={2020},
note={arXiv:2012.12294}
}
- arXiv: 2012.12294