# A challenging case of denoising+debanding.

So, I ended up making a image for fun just to find that it has a challenging case of denoising and debanding. Awful noises, and ridges can be found in this image. It’s not apparent unless you zoom in.

The challenge: Without rescaling, denoise and deband this. You’re going to run into problems like losing lots of details or residue noises or blur artifact.

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Here is my attempt

2 steps:

1. Anisotropic smoothing
2. Used smoothed image as a guide to my denoising filter.
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The challenge with your “how to deal with these artifacts” questions is, in order of increasing difficulty, that

2 Contain multiple artifacts.
3 The artifacts are structural.

The underlying problem is to generate an image with less of these, not how do we compensate for the algorithm’s flaws. Of course, in the real world, it is a combination of the two.

Yes, these are why those artifacts can’t be fixed. I found something interesting when using fourier transform. I noticed there is a lot of banding and noises. Theoretically, it can be fixable from what I look at, but I don’t know as I’m not a image processing scientist and I don’t know how to read Fourier all that well.

Just doing zoom blur on the top part of the FFT image does fix some noise problems that other methods don’t solve, but at sacrifise of details.

There are other “spaces” besides Fourier; e.g. wavelet and the other *lets. I don’t know how capable you are with the math though.

Ha ha, there is even one called

The image looks nice. But what does noise and banding mean in this case? In photography there is real light captured by a sensor. Noise is a variation from the expected “ideal” recording. Banding is a problem that arises when real analog data is converted to a digital representation. So how to decide what is noise and banding and what is the true “artificial reality” in your image?