1. A mask is a small matrix used in image filtering with weighted values that is placed over an image. 2. Convolution involves multiplying the image pixel values with the mask weights and summing to produce an output value, while cross-correlation measures similarity between images without flipping the mask. 3. Common filters include mean, Gaussian, and median filters for smoothing/noise reduction, and sharpening filters that emphasize fine details by computing intensity differences locally.

1. FILTERING AND MASKING
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2. MASK
• A mask is a small matrix whose values are
called weight.
• Each mask has an origin ,which is usually one
of its positions
• Symmetric mask
• Non symmetric mask

3. MASK
• Input image equal to output image.
• Types of mask
Convolution
Cross correlation

4. CONVOLUTION
Mask is placed on the top of the image
Mask input image pixel value multiplied with
mask weighs.
summed together to yield a single output
value that is placed in the output image at the
location of the pixel being processed on the
input

5. CONVOLUTION

6. CROSS CORRELATION
• Without flipping mask is converted to image.
• measure the similarity between images or
parts of images.
• Mask symmetric – correlation and convolution
same.

7. Cross correlation

8. FILTERING
• LINEAR FILTER
o have the property that the output is a linear
combination of the inputs
• NON LINEAR FILTER
o Erosion & dilation

9. Smoothing filter
• Low pass filter
• Noise reduction & image blurring
• Removes the finer details of image
• Types of filter
Mean filter
Gaussian filter
Median filter

10. Mean filter
• Averaging filter.
• Positive element in
mask.
• Size of the mask
determines the degree
of smoothing.

11. Gaussian filter

12. Median filter
• Used to remove the salt
and pepper noise

13. Sharpening filter
• emphasize the fine details of an image .
• Points of high contrast can be detected by
computing intensity differences in local image
regions.

14. Sharpening using derivatives
• Computing the derivative of an image has as a
result the sharpening of the image.
• The most common way to differentiate an
image is by using the gradient.
• Using gradient with finite difference has
efficient mask.