Masking is a technique of image processing in which a tiny ‘image piece’ is defined and used to affect a larger picture. Masking is the underlying procedure for several image processing techniques, including motion detection, noise reduction, and edge detection.
Masks are filters. The masking concept is also referred to as spatial filtering. Filtering is another name for masking. This idea focuses only on the filtering process done directly on the picture.
What is filtration?
Filtration is also known as the technique of convolving a mask with just an image. As this method is identical to convolution, filter masks are often referred to as convolution masks.
How it is carried out?
A filter mask is often moved from one location in an image to another while the filtering and masking procedure is carried out. The response of a filter is determined at each location (x,y) of the original picture using a predefined relationship. All the filter settings are conventional and predefined.
Varieties of filters
There are typically two sorts of filters. One is referred to as a linear filter or a smoothing filter, while the others are known as spatial frequency filters.
Why are filters used?
Image filters are utilized for different reasons. The two most frequent applications are as follows:
- Filters may be used to soften edges and reduce noise.
- For edge detection and sharpness, we apply filters
Blurring and reduction of noise
Most filters are used for distorting and noise reduction. Blurring is used in pre-processing procedures, such as the elimination of minor picture features before the extraction of major objects.
Masks for blurring
The most frequent blurring masks are.
- Box filter
- Weighted average filter
In the process of blurring, we attempt to make the transitions between various image intensities as smooth as possible while decreasing the edge content of a picture.
With blurring, it is also feasible to reduce noise.