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Sorry about the missing presets. Want to experiment with height mapping? Do you just need to make some quick and simple clouds? Then look no further! This pack contains over types of grayscale noise textures organized into eighteen different categories that can be used however you wish.
Most noise textures are also seamless on both axes. The full algorithm has a few more phases with slight variations of this basic idea. The first phase utilizes randomly placed points and is the only non-deterministic step. For a full discussion, you should read the paper [Ulichney93].
As far as I can tell, I have implemented this method exactly as described in the paper. The implementation uses Python with the SciPy stack. The algorithm maps well to such an environment because the most expensive operations are implemented very efficiently in SciPy. The implementation is well-documented and available for download. The results are really good. Figure 12 has already shown how nicely this texture tiles. The Fourier transform in Figure 15 gives an impression of how weak the low-frequency components are.
We can further analyze this by binning frequencies into radial bins and taking the mean as shown in Figure This is the typical blue noise spectrum with weak low frequencies. If we bin frequency components dependent on their angle, we get Figure 17 which nicely demonstrates just how isotropic the blue noise is.
Note that the void and cluster method has two parameters besides the output resolution. One is the standard deviation for the Gaussian filter. With larger values you get stronger low-frequency components but better isotropy. Too small values lead to ordered patterns similar to a Bayer matrix. A standard deviation of 1.
The other parameter determines how many points are placed randomly as initial seed. Everything else is deterministic, so if this value is too small you get a deterministic, ordered pattern.
Little changes once the value is moderately small. Another interesting note is that the implementation provided here does not care about the dimension of the output texture. The algorithm trivially generalizes to arbitrary dimensions. In particular, you can generate blue noise volume textures.
Just pass a tuple with the resolutions for each of the three dimensions. You can even define an anisotropic Gaussian filter with a different standard deviation for each dimension by writing GetVoidAndClusterBlueNoise 40,30,10 , 1. Note that this feature has not been tested extensively and was not intended in the original publication [Ulichney93].
Use it at your own risk. Of course, nobody likes to get the code of others running. And you do not have to. I kept my notebook busy for a few days to generate blue noise textures that should cover most needs. This way, you only have to download the textures , load them into your engine and write a few lines of shader code. I have placed them in the public domain through CC0-licensing.
All blue noise textures in the database are tileable. There are textures with a single channel of blue noise, two channels, three channels and four channels. There are many versions of most textures such that you can use a different one in each frame. Any two textures in the database have been computed with independent random numbers.
Just load all 64 of them into a texture array, pick one at random in each frame and apply a random offset in each frame. Concerning the number of channels, you should never use the same channel twice in a single frame.
Thus, if you need to dither three real quantities e. If you need more than four blue noise values, use multiple textures from the texture array in a single frame. Now we have all the blue noise we could wish for. It is time to think of ways to use it.
Let's begin with the classic. HDR televisions and monitors are on the rise but the vast majority of customers still sees your content with 8 bits per channel. Whenever you render smooth gradients, large bands will have the exact same color due to this quantization. And on most displays these bands are visible under normal viewing conditions.
As long as people have LDR displays and possibly beyond that point , dithering is the best way to work around this. Right before the quantization happens, you add blue noise to each pixel. Bands turn into finely stippled gradients that are far less distracting. As an example I have applied dithering to prefiltered single scattering with six moments. For reasons that are well-explained elsewhere it is best to transition from the uniform noise distribution to a triangular distribution before using the noise for dithering.
Also, it is important to do it in the color space in which quantization actually happens. In my case this is sRGB. The whole dithering code is given in Listing 1. The used sRGB conversion functions are shown in Listing 2. Figure 18 and Figure 19 compare single scattering without and with this blue noise dithering, respectively.
Dependent on your monitor you may or may not see the difference. In case you do not, I have brightened the images in Figure 20 and Figure As you can see dithering successfully removes the banding and leads to a very smooth end result.
A slightly less common application is jittering in ray marching. It is easy to use and available in high quality for download. Noise Dotted Texture This is a light colored noise dotted textures that can add softness to your design flawlessly.
It showcases small black dots placed in close proximity to each other. This incredible noise dotted texture is an ideal background and pattern. You can download it in various sizes by following the link mentioned below! It is a suitable texture pack for cards, wallpapers, backgrounds, PowerPoint, websites, etc. It includes 10 patterns in 1 PAT file. Noise Overlay Texture This is a high-quality noise overlay texture, which is all set to give your design a completely new meaning with its uniqueness.
It can be used as the abstract background for websites, poster, cards, etc.
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