Legacy ImageMagick Discussions Archive

Nicolas, it seems windowing Parzen with Sinc gives it a touch of added sharpness, a sigmoidal contrast of 6 seems to also tighten up the edges just enough to add some further definition. http://puu.sh/1hjaS magick rose: -colorspace rgb +sigmoidal-contrast 6 -define filter:window=sinc -define filter ...

I was playing around and this seems to be a really good filter, particularly with sigmoidal contrast in linear RGB. Standard Parzen http://puu.sh/1feXP Parzen with Linear RGB and Sigmoidal Contrast of 8 (Maybe this could be lower?) http://puu.sh/1feUT Mitchell for reference http://puu.sh/1feZl ...

I should really learn to check for that before I play with the windowing functions.

Triangle http://puu.sh/1dbJi $ magick rose: -filter triangle -resize 1000% rose_triangle.png Lanczos EWA http://puu.sh/1dbJo $ magick rose: -filter lanczos -distort resize 1000% rose_lanczos_ewa.png Catrom windowed Jinc EWA - Linear RGB and Sigmoidal Contrast http://puu.sh/1dbJs $ magick rose ...

The best way to process a two dimensional signal is with a two dimensional function as I understand.

rose-blur-box.png (1400×920) http://ompldr.org/vZnBhMw/rose-blur-box.png rose-blur-triangle.png (1400×920) http://ompldr.org/vZnBhNA/rose-blur-triangle.png rose-blur-mitchell.png (1400×920) http://ompldr.org/vZnBhNQ/rose-blur-mitchell.png rose-blur-catrom.png (1400×920) http://ompldr.org/vZnBhNg/ros ...

$ magick rose: -sample 2000% -define filter:verbose=1 -define filter:blur=20 -filter lanczos -resize 100% rose-lanczos.png # Resize Filter (for graphing) # # filter = SincFast # window = SincFast # support = 3 # window-support = 3 # scale-blur = 20 # practical-support = 60 Gives similar results to ...

http://puu.sh/19Oln $ convert rose: -sample 2000% +repage -define filter:verbose=1 -define filter:blur=20 -filter mitchell -resize 100% 00-rose-blur-mitchell.png # Resize Filter (for graphing) # # filter = Cubic # window = Box # support = 2 # window-support = 2 # scale-blur = 20 # practical-support ...

You will have to tell us what the resampling filter is and what the convolution kernel is? In general, resampling filters are nearestneighbor, bilinear (triangle), cubic convolution, lanczos, etc as described in the reference I provided. Whereas, convolution is just a blurring of the resulting ...

I have a lot to learn.

Perhaps I was too excited to develop my own filter and my naivety got the best of me. I should read more before I pursue this further.

I appreciate all the information you guys have given me.

anthony wrote:Now what is behind the decisions for the weird non-typical filter settings you have selected?

It looks good to my eyes.

Kaissel seems to work very well as a downsampler with a little blur. http://ompldr.org/vZm5qZw/fly-kaisseldown.png convert fly.jpg -colorspace rgb +sigmoidal-contrast 8 -define filter:blur=1.25 -define filter:support=4 -define filter:win-support=4 -define filter:kaiser-beta=1 -define filter:window ...

I've developed the filter into something I like to call Kaissel (kaiser windowed bessel). rose-triangle.png http://ompldr.org/vZm5obw/rose-triangle.png convert rose: -filter triangle -resize 2000% rose-triangle.png rose-mitchell.png http://ompldr.org/vZm5pMQ/rose-mitchell.png convert rose: -filter ...

I like that name!