The code for waveSharp is written in Pascal/Lazarus and although thats a "compiled" language it seems that its not the fastest for certain tasks we do. During the development of the denoising routine we found that it is slow and limited (up to 4096x4096px images). Currently we are testing a new way to run waveSharp using Python code. This allows us to use fast and speed-optimized calculation routines Python has available from several libraries. More info will be shared at #65
Although several thousand people have downloaded the freeware only a few have submitted
issues/discussions.
report your issues using https://github.com/CorBer/waveSharp/issues as they will be used for
further development. If you have very specific ideas/request please describe those in detail
when reporting.
On the 9th of december 2023 we have released the 1st beta-version of waveSharp 1.0
Further information available at https://github.com/CorBer/waveSharp/releases/tag/v1.0beta
Available at https://github.com/CorBer/waveSharp/releases/tag/v0_2
The development of the 0.2 release of waveSharp was done by Cor Berrevoets in close collaboration with:
Grant Blair
Michael Owen
Filip Szczerek
Cheng-Yang Tan
Don Capone
I want to thank them first of all for helping me get started and spending part of their free time on this. They have tested many earlier versions of the application and provided both help/bugs and ideas that helped me steering this project.
This update has the following additional features
user set processing area
cropped image saving
resized image saving
image edge padding/trimming
alignment of RGB channels (at subpixel level)
new sharpening method (bilateral filter)
automatic version checking
Cor Berrevoets
