Welcome to the world of image processing! Here are some fundamental techniques that are widely used in the field:

Common Techniques

  1. Filtering

    • Mean Filtering: Blurs an image by replacing each pixel with the average of its neighboring pixels.
    • Gaussian Filtering: Similar to mean filtering but uses a Gaussian kernel to give more weight to the central pixel.
    • Median Filtering: Uses the median of the neighboring pixel values to replace the central pixel, which is effective in removing salt and pepper noise.

  2. Edge Detection

    • Sobel Operator: Detects edges by taking the gradient of the image.
    • Canny Edge Detector: A multi-stage algorithm that uses gradient magnitude and thresholding to detect edges.

  3. Thresholding

    • Global Thresholding: Sets all pixels above a certain value to one color and all pixels below to another.
    • Otsu's Method: Automatically determines the optimal threshold by minimizing the intra-class variance.

  4. Feature Extraction

    • HOG (Histogram of Oriented Gradients): Extracts features from an image by counting occurrences of gradient orientations.
    • SIFT (Scale-Invariant Feature Transform): Detects and describes local features in images.

Example: Histogram Equalization

Histogram equalization is a technique used to improve the contrast of an image. It redistributes the pixel intensities of an image to more evenly distribute the range of intensity values.

How it Works

  1. Compute the histogram of the image.
  2. Normalize the histogram.
  3. Calculate the cumulative distribution function (CDF) of the normalized histogram.
  4. Apply the CDF to the image to equalize the histogram.

Histogram Equalization Example

For more information on histogram equalization, you can read our detailed tutorial on Histogram Equalization.

Conclusion

Image processing techniques play a crucial role in various applications, from medical imaging to computer vision. By understanding these techniques, you can unlock the full potential of image processing.