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Image preprocessing is a crucial step in preparing images for machine learning tasks. It involves various techniques to enhance the quality of images, reduce noise, and extract meaningful features.
I. The Importance of Image Preprocessing:
1. Normalization and Standardization:
- Reason: Models often perform better when input features are normalized or standardized.
- Example: Dividing pixel values by 255.0 normalizes them to the range [0, 1].
2. Size Standardization:
- Reason: Models typically expect input images of a consistent size.
- Example: Resizing images to a standard size ensures uniformity in the dataset.
3. Noise Reduction:
- Reason: Removing noise helps the model focus on relevant patterns.
- Example: Applying Gaussian blur or contrast stretching can reduce the impact of noise.
4. Feature Enhancement:
- Reason: Enhancing features helps the model identify patterns more effectively.
- Example: Histogram…
