Image preprocessing is a crucial step in computer vision and image analysis, involving various techniques to enhance the quality of images for subsequent tasks. Here are 75+ things to do for image preprocessing:
- Grayscale conversion: Converting color images to grayscale simplifies processing and reduces computational requirements.
- Image resizing: Changing the dimensions of an image is common for standardizing input sizes in machine learning models.
- Normalization: Scaling pixel values to a standardized range (e.g., 0 to 1) helps in model convergence.
- Contrast adjustment: Enhancing or reducing image contrast improves visibility of features.
- Histogram equalization: Balancing the distribution of pixel intensities enhances contrast in images.
- Gamma correction: Adjusting gamma values helps correct brightness variations in images.
- Image rotation: Rotating images can provide multiple perspectives for analysis.
- Image cropping: Removing irrelevant or redundant portions of an image focuses on relevant features.
- Image flipping: Flipping images horizontally or vertically increases the diversity of training data.
- Padding: Adding extra pixels…