OverSampling in Image Processing
Oversampling is a method used in image processing to improve the resolution of a picture. It entails collecting the image with a greater sample density (more pixels per unit area) than the final image resolution. Oversampling spreads the light over more pixels than are required to attain full resolution, increasing imaging time. Oversampling a picture properly may efficiently eliminate a considerable amount of road noise, but oversampling too much can result in spurious image features and increase the time required for image processing.
The main purpose of performing Oversampling is:
- Noise Reduction
- Increase Resolution
- To improve anti-aliasing performance
- Anti-aliasing is a technique for removing jaggies that appear in Computer gaming objects. There are various anti-aliasing solutions, each with its own set of advantages and disadvantages. Most PC games feature an in-game window where you may change visual settings like anti-aliasing.
Mathematically executing oversampling in image processing:
To accomplish mathematical oversampling in image processing, we must interpolate the picture to increase the number of pixels in it. This can be accomplished through the use of several interpolation techniques, such as bilinear or bicubic interpolation.
Bilinear Interpolation: The value of a new pixel in bilinear interpolation is determined as a weighted average of the values of its four nearby pixels in the original picture.
Bicubic Interpolation: A comparable weighted average is generated using a 4x4 pixel region surrounding the new pixel in bicubic interpolation.
The amount by which the picture is interpolated is determined by the oversampling factor. For example, if the original image has a resolution of 500x500 pixels and we wish to oversample it by a factor of two, we would interpolate it to get a resolution of 1000x1000 pixels.
That’s it for this week! Next week I will be back with some other content. Till then BBye!
