75+ Things to Do For Image Processing and Analysis

btd
5 min readNov 28, 2023
Photo by plo olq on Unsplash

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:

  1. Grayscale conversion: Converting color images to grayscale simplifies processing and reduces computational requirements.
  2. Image resizing: Changing the dimensions of an image is common for standardizing input sizes in machine learning models.
  3. Normalization: Scaling pixel values to a standardized range (e.g., 0 to 1) helps in model convergence.
  4. Contrast adjustment: Enhancing or reducing image contrast improves visibility of features.
  5. Histogram equalization: Balancing the distribution of pixel intensities enhances contrast in images.
  6. Gamma correction: Adjusting gamma values helps correct brightness variations in images.
  7. Image rotation: Rotating images can provide multiple perspectives for analysis.
  8. Image cropping: Removing irrelevant or redundant portions of an image focuses on relevant features.
  9. Image flipping: Flipping images horizontally or vertically increases the diversity of training data.
  10. Padding: Adding extra pixels around the image can be useful to maintain information at the borders.
  11. Image smoothing: Applying filters like Gaussian blur reduces noise and emphasizes essential features.
  12. Image sharpening: Enhancing edges in images can improve feature detection.
  13. Edge detection: Identifying edges in images is crucial for object boundary detection.
  14. Thresholding: Converting grayscale images to binary images based on intensity thresholds simplifies analysis.
  15. Image inversion: Inverting pixel values can reveal hidden details in certain types of images.
  16. Color space conversion: Switching between color spaces (e.g., RGB to HSV) can highlight specific color information.
  17. Image denoising: Reducing noise in images improves the accuracy of subsequent analyses.
  18. Image normalization: Standardizing pixel values across…

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