The author of this tutorial is Philippe Gregoire, a Belgian digital painter, which works in Photoshop, Poser, and Corel Painter.
Attention! The tutorial was made with the old version of the Noise Buster algorithm. Starting from version 12.0, the program uses trained neural networks to remove noise. You can see the work of Noise Buster AI here.
To get rid of noise and pixelation, Philippe used AKVIS Noise Buster. Now a specialized freeware program for jpeg artifact removing is available — AKVIS Artifact Remover AI.
|Fragment Before: Noise Photo||Fragment After: Enhanced Photo|
A few weeks ago, I was told by a friend about a plug-in called AKVIS Noise Buster that could help me with my picture noise problems.
For my work I always start from photos I take by myself and I also do a lot of scans. My main problem is to improve the photo and scan quality first because all the scanned pictures have always a lot of noise especially those that come from prints. Photos taken under bad light conditions also have a lot of noise.
The only solution I had in Photoshop was to Gaussian blur and then to Unsharp Mask my source images. I was also struggling with PS noise filter to remove remaining noise. But the main point with all these tools is that whatever you try, you always loose picture sharpness and details in the end.
I then tried AKVIS Noise Buster and on my first attempt, I was immediately amazed. It does a great job of smoothing my images without losing sharpness. For example, in a close-up view of an image where you can see all of the skin imperfections, AKVIS Noise Buster transforms it to an image where the subject has smooth young skin while preserving the sharpness of the eyes, lips, and other details.
Sometimes you get a very bad JPEG image file.
At first sight it looks not so bad, but if you zoom a little, you'll see the typically square-pixellated design which can be found on bad JPEG's.
That's when the AKVIS Noise Buster plug-in can be very helpful.
Click and again and you get a more corrected image. Maybe you will find this one is corrected too much.
you see that the correction is good on some parts of the image (the posts and the sign) and too strong on some other parts (trees, grass...).
The solution will be to isolate these parts from one another.