A different approach to scanned image histograms

[barnacle]

Here is is with a scale of 3. Struan, I'm interested in your log function if you'd care to detail it. I think what I have here is an inverse exponential: what I was after was making the dimmest lines bright enough to see but successive lines provide less input. With a scale of 2, the outputs available are 128, 196, 228, 244, 252, 254, and 255 (I think) so anywhere that gets more than seven or eight hits is full brightness. That's extended for larger divisors, and the dimmest lines are proportionally dimmer.

Nicholas, the brightest part of this image, apart from the white surround, is on the pillars to the right of the door.

Neil

[Struan Gray]

Just take log(1+pixel_value) and rescale so the maximum is at 255.

This compresses the highlights and expands the shadows, but leaves the spatial structure comprehensible. It doesn't do much that is useful for normal pictorial images, but is used a lot for data (particularly Fourier transforms, where one prominent peak can hide everything else). I found it helpful for quick assessment of the kinds of distributed histograms we're discussing here. It's a toggle control on most image processing packages, so you can quickly switch back and forth.

I used brute force, changing values to double precision, and then rescaling back to integer/byte. Since you know your wuffums are 8-bit and pre-scaled, you could probably do the log function as a look up table.

[barnacle]

Thanks Struan.

This version just increments the histogram linearly in the collection phase, then applies the log function in the display phase.

Using a straight log(10) still leaves the single trace a bit gloomy:



Adding an offset and scaling the log a little less lifts the black too much:



So gating out the zero samples seems an improvement:




if brightness[brightptr + x] > 0:
green = 64 + int(75 * math.log(brightness[brightptr + x] + 1, 10))


Neil

[Struan Gray]

FWIW, I used to use the log scaling a lot more often when applying a universal normalisation. When normalising each histogram separately, there's less need to bring up the shadows, and as you've shown, when overdone it ends up making the overall shape of the data harder to interpret.

You have a nice tool there. I look forward to downloading a copy :-)

[barnacle]

Thanks. I'm investigating how to write plugins for Gimp; I'll post again once there's something to see.

Neil