A sophisticated technique that redistributes pixel intensity probabilities. It is vital for images with low contrast, effectively "stretching" the range of the image to cover the full grayscale spectrum.
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Using kernels (small matrices) to blur or sharpen images. A Mean Filter reduces noise by averaging pixel neighborhoods, while a Laplacian Filter enhances edges by detecting rapid changes in intensity. 2. Geometric Transformations Using kernels (small matrices) to blur or sharpen images
Using Gaussian blurring to remove high-frequency noise. 4. Conclusion and rotations while preserving collinearity.
The simplest form of enhancement, where each pixel is modified based solely on its own value. Common examples include brightness adjustment and contrast stretching.
💡 Image enhancement improves clarity , while geometric transformation ensures spatial accuracy .
These include translations, shears, and rotations while preserving collinearity. They are the mathematical foundation for "rectifying" images taken from tilted angles. 3. Practical Implementation and Tools