Information Technology IT – 19 Graphics creation | e-Consult

Here are three techniques for reducing noise in a bitmap image:

• Median Filtering:

  Principle: Median filtering replaces each pixel value with the median value of its neighboring pixels. This is particularly effective at removing 'salt and pepper' noise (random black and white pixels) because the median is less susceptible to outliers.

  Advantages: Effective at removing impulsive noise, preserves edges better than mean filtering. Relatively simple to implement.

  Disadvantages: Can blur the image, especially with large kernel sizes. May not be as effective for Gaussian noise.

• Mean (Averaging) Filtering:

  Principle: Mean filtering replaces each pixel value with the average value of its neighboring pixels. This smooths the image by averaging out the pixel values.

  Advantages: Simple to implement, effective at reducing noise.

  Disadvantages: Blurs edges significantly, can smear details. Not effective for removing salt and pepper noise.

• Gaussian Filtering:

  Principle: Gaussian filtering uses a Gaussian function to weight the neighboring pixels. Pixels closer to the center of the kernel have a higher weight, resulting in a smoother, more natural blur than mean filtering.

  Advantages: Reduces noise while preserving edges better than mean filtering. Provides a more natural-looking blur.

  Disadvantages: More computationally expensive than mean or median filtering. Requires careful selection of the standard deviation (sigma) value to achieve the desired level of noise reduction without excessive blurring.