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Convolution
Till now, we have learned an image is a visual representation,
typically made up of tiny elements called pixels. When many
pixels are arranged together in a grid, they form a complete
image that we can see and recognise. Each pixel has a value
ranging from 0 to 255 to specify color and brightness. Normal 1977 Aden
The computer stores and image in number. But what happens
if we modify these numbers? The answer is simple: the image
changes. Altering pixel values directly impacts how the image
looks, and this concept forms the foundation of image editing.
Today, we commonly use various image editing tools like
Brannan Brooklyn Calrendon
Photoshop, along with apps such as Instagram and Snapchat,
to enhance images by applying filters. These filters work by
uniformly modifying the pixel values across the entire image,
resulting in visual transformations.
But how do these filters achieve such effects? This is
accomplished using a mathematical process called Earlybird Gingham Hudson
convolution, which involves a convolution operator to manipulate pixel values and create the desired changes.
Convolution is not only essential for applying filters but also plays a significant role in image processing tasks
like sharpening, blurring, and edge detection. By altering pixel values systematically through convolution, we
can enhance image quality, add creative effects, or even extract critical features for advanced applications like
Computer Vision.
In simple terms, convolution is passing a "kernel" matrix over the whole "image" matrix to give the convoluted
matrix i.e., the filtered image.
Technically, convolution is defined as a simple Mathematical operation that multiplies two numeric arrays of the
same dimensions but different sizes to produce a third numeric array of the same dimensions. For example:
1 1 1 0 0 0 1
1 0 1 0 1 0 0 4 5 3 2 2
x1 x1 x0
1 1 0 1 1 0 1 2 4 3 4 2
x0 x1 x1
1 1 0
0 1 0 1 0 1 0 4 4 3 5 3
x1 x0 x1
0 1 1 1 0 1 1 * 0 1 1 = 4 5 3 4 4
1 0 1
1 1 0 1 1 1 0 4 3 6 4 3
1 0 1 0 1 1 0
Image Matrix Kernel Matrix Convoluted Matrix/Desired image
A B C
The resulting pixel is calculated as:
(0×1) + (1×1) + (0×0) + (1×0) + (1×1) + (0×1) + (1×1) + (0×0) + (1×1) = 4
Kernel
Kernel is also known as a Convolution Matrix or mask (typically a 3x3 or 5x5 matrix) will help you in image
processing by creating a wide range of effects like sharp, blur, masking etc. The kernel is slid across the image
and multiplied with the input image matrix to generate an output image with an enhanced desired effect.
346 Touchpad Artificial Intelligence (Ver. 3.0)-X

