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Wavelet denoising

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-3776AB?logo=python&logoColor=white)](https://www.python.org/)

Result

Sr	Scene	Gaussian	Bilateral	VisuShrink soft	BayesShrink soft
1	black panther · sparsity 0.788	24.54	26.08	20.39	23.82
2	warthogs drinking · sparsity 0.855	26.44	27.41	22.57	25.29
3	cormorants nesting · sparsity 0.921	25.91	28.16	22.51	25.55
4	taj mahal reflected · sparsity 0.947	25.52	28.15	22.19	25.63

The finding

The premise, measured before anything depends on it

The gap is real and large. A perfectly flat spectrum would put 0.10 in the top 10%; noise scores 0.44 because the largest samples of a Gaussian are still larger than the rest, and every photograph scores 0.69 or above. **Thresholding really can separate them.**

That is what makes the result interesting rather than disappointing: the method's justification is sound, and the Haar basis is still not a good enough one.

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Figures

Elsewhere

Code and full results on GitHub (code private)

Every number is read from the file the run produced. github.com/hammasbuilds · kaggle.com/muhammadhammas13