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Extremely abstract and highly saturated version of a glitched and remixed painting by Tiziano Vecellio, the result of various processes, including an FFT that cut off high-frequency detail.
Animated GIF version here.
Created with GlitchSort. Remix and mashup of commons.wikimedia.org/wiki/File:SundaySchool.jpg, by Tom Mandel, licensed under the terms of the cc-by-sa-2.0.
Image processing stages - left to right, top to bottom:
1. raw image from registax6 immediately after stacking
2. registax wavelets - settings: gaussian wavelets, increment 1, wave 2 slider slightly negative, wave 3 slider range 4-10
3. registax6 histogram and colour balance: use the histogram slider to adjust the midpoint, and also stretch the dynamic range (log scale view to avoid clipping).
Use colour balance panel to turn down the red and blue channels for correct-ish eyeball colour.
4. restart registax6 and import the saved .png image. Gentle use of wavelets, same settings again. Wave 3 slider range 2-4
5. registax6 histogram and colour balance: same procedure as before
6. import image into Photoshop elements. Gaussian blur at minimum settings (0.4/0.5) to remove noise and obvious pixelation/TV scan lines.
3 rounds of High Pass Masking (overlay setting - hard light setting is too fierce, soft light too modest) and if necessary a bit of Gaussian blur if speckles start to intrude.
No idea what HPM does - works like a much gentler version of unsharp masking, and only 1 parameter to adjust !
7. More Photoshop Elements: adjust levels RGB midpoint for brightness, Red and Blue channels for colour balance again.
Mosaic made with ACS data
www.robgendlerastropics.com/NGC4449-HST-Gendler.html
Robert Gendler
email: robgendler@att.net
Website: www.robgendlerastropics.com
Visualising SIFT descriptors (on frames from Wall-E) more description of the method (using openCV and Python) at my blog: www.cutsquash.com/2014/11/visualising-sift-descriptors/
Detail of Little Boy 006. Glitch, remix of commons.wikimedia.org/wiki/File:Atombombe_Little_Boy_2.jpg, an image of the Little Boy atom bomb. Here ithe image has been eroded until it disappears.
The color effects of the image glitching/erosion method I'm using are somewhat more apparent here than in others. the colors seem to sum to gray when high resolution images are reduced in size.
Experimenting with colour versions of the classic image processing technique of the Hough transform.
See more details and examples here:
www.cutsquash.com/2014/02/colour-hough-transform/
Code available here:
Glitch and remix of a painting by Tiziano Vecellio (Titian). The digitized version had highly saturated color, making it an interesting candidate for color and detail degrading with an FFT. The degrading of detail is especially apparent here.
Technical note: Noise filtered with an FFT at various frequency bands, with sorting, quantization, channel-swapping and some masking/remixing and editing tricks. A heavily glitched image intervened to create the lighter and darker areas and some of the color, otherwise it had little effect.
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Process: I filtered out high frequencies ("details") using a statistical FFT at different scales on each color channel (RGB), then I attempted to recover the image by enhancing the remaining high frequency data (using a statistical FFT with different settings), but pushed the recuperation process beyond the limits of "good" imaging.
ACS data
www.robgendlerastropics.com/M22-HST-Gendler.html
Rob Gendler
email: robgendler@att.net
Website: www.robgendlerastropics.com