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Recent Posts
- Dimensionality reduction for image and texture set compression May 21, 2020
- “Optimizing” blue noise dithering – backpropagation through Fourier transform and sorting April 26, 2020
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- Using JAX, numpy, and optimization techniques to improve separable image filters March 15, 2020
- Separate your filters! Separability, SVD and low-rank approximation of 2D image processing filters February 3, 2020
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Categories
Tag Archives: supersampling
Checkerboard rendering, rotated anti-aliasing and grid frequencies
This post is inspired by three interesting statements or questions that I heard and are interconnected by non-obvious theory: First was regarding so called checkerboard rendering (example1 example2) – what is advantage of using it? After all it’s the same as … Continue reading
Posted in Code / Graphics
Tagged antialiasing, fourier, frequency domain, sampling, signal theory, supersampling
4 Comments
Major C#/.NET graphics framework update + volumetric fog code!
As I already promised too many times, here comes major CSharpRenderer framework update! As always, all code available on GitHub. Note that the goal is still the same – not to write most beautiful or fast code, but to provide a … Continue reading
Posted in Code / Graphics
Tagged .NET, algorithms, C#, graphics, graphics programming, postprocessing, supersampling, temporal, temporal supersampling
18 Comments
Poisson disk/square sampling generator for rendering
I have just submitted onto GitHub small new script – Poisson-like distribution sampling generator suited for various typical rendering scenarios. Unlike other small generators available it supports many sampling patterns – disk, disk with a central tap, square, repeating grid. It … Continue reading
Posted in Code / Graphics
Tagged algorithms, Anaconda, programming, python, stochastic, supersampling, temporal, tools, winpython
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Temporal supersampling and antialiasing
Aliasing problem Before I address temporal supersampling, just a quick reminder on what aliasing is. Aliasing is a problem that is very well defined in signal theory. According to the general sampling theorem we need to have our signal spectrum … Continue reading
