Image Filtering is used everywhere nowadays. Right from social media applications and news articles to various training models to improve image data. It can be used to remove noise from images as well as smoothen edges.
To understand image filtering, let us first understand image blurring. Starting with Gaussian Blur
In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. The name is derived from the mathematician and scientist Carl Friedrich Gauss and is also known as Gaussian Blurring.
As I stated before, the Gaussian blur is a…
Following up on my previous article on getting crisper images by manual resizing, another method used is through interpolation. Interpolation is a method of constructing new data points within the range of a discrete set of known data points. We interpolate (or estimate) the value of that function for an intermediate value of the independent variable.
There are various types of interpolation. Let’s focus on three of them —
This type of interpolation is the most basic. We simply interpolate the nearest pixel to the current pixel. Assuming, we index the values of the pixels from 0. …
When training vision models, it is common to resize images to a lower dimension ((224 x 224), (299 x 299), etc.) to allow mini-batch learning and also to keep up the compute limitations. It improves efficiency and typically leads to better results.
Another idea which we will explore today is that rescaling can be used to reduce the blur and increase the quality of an image. This is because when we downscale an image, we not only reduce the blur but also lose small details (since the pixel size is the same, anything that becomes smaller than one pixel will…
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