Deep Learning CNN Convolutional Neural Networks with Python - Image Blurring 2

The video tutorial explains the concept of image blurring using a 3x3 averaging mask. It describes the process of designing a filter with 1/9 values and how it is applied to an image through a sliding window technique. The tutorial also covers advanced filtering techniques, including zero padding, and introduces non-uniform filters like the Gaussian filter, which is commonly used for noise reduction and image smoothing.

What is the primary purpose of using a 3x3 averaging mask on an image?

• To change the image color

• To blur the image

• To sharpen the image

• To increase the image contrast

How is the 3x3 averaging filter applied to an image?

• By adding the filter values to the image

• By subtracting the filter values from the image

• By multiplying the filter values with the image values and summing them

• By dividing the image values by the filter values

What happens when a filter is placed at the edge of an image?

• The filter is ignored

• The filter is resized

• The image is cropped

• Zero padding is used

What is a key property of an averaging filter?

• The filter values must be negative

• The sum of all filter values must be one

• The filter values must be greater than one

• The sum of all filter values must be zero

Which filter is commonly used for image blurring and noise reduction?

• Sobel filter

• Median filter

• Laplacian filter

• Gaussian filter

Why is the Gaussian filter preferred in many image processing tasks?

• It is easier to implement

• It uses less memory

• It is faster to compute

• It has good properties related to the central limit theorem

What is the effect of smoothing an image?

• It changes the image color

• It enhances the image details

• It increases the image noise

• It reduces the image noise