When to Use Contrast as a Preprocessing Step

Enhancing image contrast is a fundamental preprocessing technique in computer vision to improve model performance by making edges and details more discernible. Contrast involves adjusting pixel values relative to each other, rather than uniformly altering brightness, which is particularly useful in fields like astronomy and tasks like optical character recognition (OCR) where clarity is crucial. Preprocessing, which applies transformations across all datasets, contrasts with augmentation that is limited to training sets. Tools like TensorFlow, PyTorch, FastAI, and scikit-image provide various methods for contrast adjustment, with adaptive histogram equalization being a notable approach for evenly spreading pixel values. The article highlights that while these tools aid in preprocessing, consistency across different frameworks is vital for effective experimentation and model development.