Image Preprocessing

Certain preprocessing steps can make digital image correlation more robust to differences in brightness and contrast between a reference and deformed image. This page covers two of them, using the astronaut reference image from the previous page as an example.

Brightness

Brightness shifts the entire pixel-intensity histogram up or down by a constant amount — the whole image gets lighter or darker together, dark areas included. Pushed too far, dark regions wash out to a flat gray and highlights clip at pure white (255), permanently losing detail.

import dictk
from dictk.image import brightness, histogram_save, write

photo = dictk.astronaut(width=300, height=300)
write(arr=photo, path="astronaut_original.png")
histogram_save(arr=photo, path="astronaut_original_histogram.png")

bright_1_5 = brightness(arr=photo, factor=1.5)
write(arr=bright_1_5, path="astronaut_brightness_1.5.png")
histogram_save(arr=bright_1_5, path="astronaut_brightness_1.5_histogram.png")

bright_2_0 = brightness(arr=photo, factor=2.0)
write(arr=bright_2_0, path="astronaut_brightness_2.0.png")
histogram_save(arr=bright_2_0, path="astronaut_brightness_2.0_histogram.png")

Saved: astronaut_original.png, astronaut_brightness_1.5.png, astronaut_brightness_2.0.png

factor=1.0 (original)	factor=1.5	factor=2.0

factor=1.0 (original)	factor=1.5	factor=2.0

At factor=1.5 the histogram shifts right as a whole — midtones move into the brighter half and the mean climbs, with a few highlights starting to clip at 255. At factor=2.0 the shift is large enough that a big share of pixels pile up at that 255 ceiling, visible as a tall spike at the histogram's right edge: real detail that's been clipped away and can't be recovered.

Contrast

Contrast is the spread between an image's darkest and lightest pixels. Increasing contrast stretches the histogram outward from its own mean — darks get darker, lights get lighter — while the mean itself stays roughly where it was.

import dictk
from dictk.image import contrast, histogram_save, write

photo = dictk.astronaut(width=300, height=300)

contrast_1_5 = contrast(arr=photo, factor=1.5)
write(arr=contrast_1_5, path="astronaut_contrast_1.5.png")
histogram_save(arr=contrast_1_5, path="astronaut_contrast_1.5_histogram.png")

contrast_2_0 = contrast(arr=photo, factor=2.0)
write(arr=contrast_2_0, path="astronaut_contrast_2.0.png")
histogram_save(arr=contrast_2_0, path="astronaut_contrast_2.0_histogram.png")

Saved: astronaut_contrast_1.5.png, astronaut_contrast_2.0.png

factor=1.0 (original)	factor=1.5	factor=2.0

factor=1.0 (original)	factor=1.5	factor=2.0

At factor=1.5 the histogram spreads outward from the mean rather than shifting — the astronaut's silhouette and helmet edges get sharper, while the mean barely moves. At factor=2.0 the spread is wide enough that more pixels pile up at both the 0 and 255 ends, crushing fine midtone detail even as high-contrast edges sharpen further.

Key Insight: Contrast stretches the histogram, while brightness translates it.