Image blur using matrix

1. IMAGE BLUR USING MATRIX
DONE BY
Mohamed mukhthaseem [111622102095]
M Jayanth [111622102099]
Msv Chitesh [ 111622102100]
M MUNUSAMI [ 111622102101]

2. IMAGE BLUR LOGIC:
MAIN IDEA
CONVOLUTION KERNEL
GAUSSIAN BLUR
IMPLEMENTATION

3. MAIN IDEA
 The basic idea of blurring an image is that for each pixel, we average it with its
neighbour's and use a low-pass filter to reduce the amount of noise and detail in an
image.
 Image blurring is used to make an image smooth in which edges of that image are not
observed.
 By reducing the detail in an image we can more easily find objects that we are
interested in.
 Furthermore, this allows us to focus on the larger structural objects in the image.
 Most common in computer vision and image processing.

4. STEPS TO BLUR AN IMAGE
STEP 1: Retrieve the values of the centre pixel and its neighbours
STEP 2: In image Processing, we are manipulating one pixel at a time which means that
we are multiplying
STEP 3: Add them together
STEP 4: Divide by the amount of pixels we just added
STEP 5: Assign the resulting value to the centre pixel

5. CONVOLUTION
 Convolution is simply the process
of taking a small matrix and
running it over all the pixels in an
image.
 At every pixel, we’ll perform some
math operation involving the
values in the convolution matrix
and the values of a pixel and its
surroundings
 This is done to determine the value for a
pixel in the output image.

6. GAUSSIAN BLUR
 Imagine that this distribution is superimposed over a
group of pixels in an image.
 It should be apparent looking at this graph, that if we
took a weighted average of the pixel’s values and the
height of the curve
 This is, in essence, how Gaussian blur works.
 TLDR: A Gaussian blur is applied by convolving the
image with a Gaussian function.

7. Example of gaussian blur

8. IMPLEMENTATION
 We’ll need to create a separate output image.
 We can’t modify the source image directly because changing the pixel
values will mess up the math for the adjacent pixel’s computation in the
next iteration.
 Finally, we need to consider how we’ll handle the edges. If we were looking
at the very first pixel in an image, the kernel would extend beyond the
bounds of the image.
 As a result, implementations will commonly ignore the outer most set of
pixels, duplicate the edge, or wrap the image around.

9.  In our case, for ease of implementation, we’ll
ignore it pixels on the edges.
 Let’s start with implementing the Gaussian
function. The first task is to identify reasonable
values for x, y, and σ.
 While the kernel can technically be an arbitrary
size, we should scale σ in proportion to the kernel
size.

10. PROGRAM

11. ORGINAL
PICTURE

12. RADIUS:3

13. RADIUS:5

14. RADIUS:11

15. THANK YOU