8 scikit-image Tricks That Supercharge CV Models

Practical preprocessing moves that clean data, amplify signal, and make your computer-vision models learn faster with fewer surprises.

Thinking Loop

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Eight scikit-image preprocessing tricks — illumination fixes, CLAHE, denoise, edge-aware sharpen, masks, and more — to boost computer-vision model accuracy.

You can often win 5–20% accuracy without touching the model.
Tidy pixels → simpler features → calmer gradients. And let’s be real, cleaner inputs also make debugging less soul-crushing.

Below are eight scikit-image techniques I lean on when training classification, detection, and OCR systems. Each is small, testable, and easy to roll back.

1) Fix Uneven Lighting with Background Subtraction

Harsh lighting turns easy problems into class confusion. Remove the low-frequency background and your model “sees” the object, not the glare.

When to use: document scans, product photos under downlights, thin objects on glossy tables.

import numpy as np
from skimage import morphology, filters, img_as_float

def flatfield_correct(img_gray, radius=50):
    img = img_as_float(img_gray)…