A Dangerous Size

Printing an 11 x 14 inch sheet on the Epson 9900 involves a ticklish problem: the paper jams against the roll cover. This crumples it top and probably bottom, where the crumpled paper can hit the print head. This could damage the print head. Collisions can happen both when the paper feeds for the first time, when it moves up and down as the sensors on the head measure and position it, or when you start printing, and it again adjusts up and down.

Fortunately, the solution is simple. You can hover over the printer, gently guiding the top edge of the paper with your hands, though that isn’t the best solution. Holding or taping another piece of paper onto the roll cover behind the sheet works better. You could also leave the cover in raised position, but then you wouldn’t have it to guide and support the sheet.

protected printer

Paper taped to roll cover

Bitmap to Surface to Model

Bitmaps can be used as height fields for a 3D terrain. Typically, the brighter the pixel value, the higher the terrain at the location mapped to the pixel. A 3D grid such as a terrain model can be output as cross-sections and created in any convenient material. The Processing app included with this post will do the whole process for you: open a bitmap, display it as a terrain model, and output cross-section profiles to a PDF file. The profiles include slot marks. Once you cut out the profiles and the slots, you can fit everything together into a model. The model can be used to construct a mold for the surface.

Let me explain this with pictures. First, here’s what the assembled model looks like:

The assembled model

The assembled model

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Mushroom Duotones

Posted a few duotone images of mushrooms to Flickr. Didn’t mean to spend this morning making these images, but when you get the right subject matter, there is an eerie presence to toned images: Maybe it’s the intersection of image qualities that recall old photography and emulsions hand-coated onto paper coupled with high tech display and printing—or maybe the mushrooms (just shitakes) have an effect on me.

This tutorial on duotones over at Luminous Landscape was useful.

Duotone Soft Proof

Can’t soft proof a duotone in Photoshop. Have to convert to RGB to soft proof; however, the color changes in subtle ways.

One way to compare the images is by checking their histograms.

The duotone image is just a little darker on average (mean 106.97 compared to mean 112.53), and it clearly has pixels in the darkest bins, including a spike of 0% black over on the left edge of its histogram. The RGB image has no black pixels until you reach level 11, about a 4% gray.

Comparison of two histograms

Comparison of RGB and duotone image histograms

You can use Curves adjustment layers to compare the dynamic ranges of the two versions of the image. Option-drag on the black point or the white point in the Curves dialog to see where in the image the darkest and lightest values are found. The duotone reveals solid black (0%) in a few places. The RGB version has no solid blacks. In the light tones, the two are practically the same.
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Bezier Circle

Approximate a circle with cubic Bezier curves: here’s a simple method, written in Processing. The key to approximating the circle is a constant, kappa, that will help us calculate the distance from a Bezier anchor point on the circle to its associated control point, off the circle. Kappa is the distance between the anchor point and the control point divided by the circle radius, when the circle is divided into 4 sectors of 90 degrees. See these notes by G. Adam Stanislav for the math.

Circle approximated by five Bezier curves. Click to view applet.

Notes: Kappa is scaled by the number of sectors into which the circle is divided (k = 4 * kappa / sectors). Four sectors is the minimum required to get a good approximation. With a little bit of work it should be possible to create a circle with unequal sectors, scaling kappa by the portion of the circle occupied by each sector. From there, one could make various sorts of blobs by scaling the the control points and anchor points around the center of the original circle.

Lab Enhancements

Using Photoshop’s Lab color mode, you can perform a number of simple image enhancements. For some of these enhancements there are similar RGB operations; however, the results are subtly (and sometimes not-so-subtly) different. The international standard L*a*b* color space from which the Lab mode in Photoshop was derived was constructed to capture the range of human vision. It was based on statistical evaluations of the range of color vision (the “a” and “b” channels)  and of just-perceptible differences in brightness (the Lightness channel).

Several techniques are illustrated by Photoshop actions that you can download. Explanations and a few tips on how to perform the actions manually follow.

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Lab Lightness

Lab color mode can be used for brightness, contrast, and color enhancements in somewhat different ways than RGB mode. Where RGB mode provides a composite channel (RGB) that is composed of red, green and blue channels, Lab mode provides a composite channel (Lab) that is a composite of Lightness, “a” and “b.” The a and b channels encode the color information, while the Lightness channel, as it name suggests, encodes the grayscale values.

Image of a man, showing Lab and lightness channels

Sample image with Lab and lightness channels

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Fresnel Ring Moires

This Processing applet shows how overlapping Fresnel rings create moiré patterns that are either straight lines or Fresnel rings. The animation moves the two Fresnel rings together and apart at a constant rate, but the moiré patterns accelerate and decelerate. Animation works best when your browser isn’t doing a lot of other tasks that interrupt the applet–download it for best results.



Click in the applet and type “m” to use the mouse to drag the rings left and right. Type “a” to animate again. Source Code: moire. Made with Processing.

The Fresnel rings are drawn with circles using different stroke widths and no fill. The stroke widths derived by subtracting the diameters of a series of circles proportional to successive square roots of a sequence of integers 1..N.

  for (int i = 0; i < rings; i++) {
    diameters[i] = (float) Math.sqrt(i + 1) * max_diameter;
  }

Fresnel lenses are composed of prisms arrayed in Fresnel rings. In the 50s, plastic lens that you could slap onto your teevee to magnify the picture were popular. Large ones are used in solar cookers. The advantage over regular lenses is that Fresnels can be much lighter. They can also be built of modules that fit into a frame--this made them very efficient for focusing lighthouse lights, as they could be transported in sections and assembled on site.

Incidentally, putting a Java applet (from Processing) into a WordPress post was not simple. Following advice in the Processing forum, I resorted to the (deprecated) <applet> tag instead of using the markup generated by Processing. If anyone knows how to use that more current markup (or something similar), please post a comment. I had to do my final edits in the HTML editor and insert the <applet> tag with no linebreaks. Do not go back the visual editor if you do this--it will clobber the applet markup (you could probably fine tune MCEdit to get around this).

You can see this applet its own page, generated by Processing, here.

Night Noise

You can reduce the noise in low light photography by taking multiple images and merging them to extract the statistical mean or median value. These statistical operations are available in Photoshop’s Layers > Smart Objects > Stack Mode menu. Here is a clear example of the effects of the mean and median operations on a stack of four similar images.

three images showing noise reduction by mean and median operations

Original, mean, and median images compared

The leftmost image is one of four similar images that were stacked into a smart object. In the middle image, the mean value (average) of the four images is used as the value of at each pixel position. In the right image, the median value (midpoint of the range of values) is used. You can find the full median image here.

The noise reduction is pretty dramatic (you’ll have to click on the image and view it full size to see what I mean). I find the mean image somewhat smoother, visually, than the median image, but the median image has some advantages over the mean.

A man in a yellow rainjacket walked across the view while I was shooting. You can see four images of him in the mean image: logically, one fourth of the pixels  in the stack at those points belonged to the moving man, so he has a ghostly presence. In the median image, he has practically disappeared: the influence of details or noise that appear in only one of the images is much less marked than in the mean image. The median operation is particularly useful for removing momentary details from a statistical composite.

Of course, the reason the original images were so noisy is that I was shooting hand-held at a high ISO (1600). If I had used a tripod and lower ISO with time exposure, I would not have had to resort to statistical operations for noise reduction. Knowing that the image would be noisy, I shot multiple images and used Photoshop’s File > Automate > Photomerge… command to position the images in layers. Details of how to do this can be found in an earlier post, Statistical Blending.