Abstract
An understanding of frequency domain and linear filtering concepts is essential to be able to comprehend significant topics such as image and video enhancement, restoration, compression, segmentation, and wavelet-based methods. Exploring these ideas in a 2D setting has the advantage that frequency domain concepts and transforms can be visualized as images, often enhancing the accessibility of ideas. The basic theories in two dimensions (2D) are founded on the same principles. However, there are some extensions. For example, a 2D frequency component, or sinusoidal function, is characterized not only by its location and its frequency of oscillation but also by its direction of oscillation. The 2D DSFT is the basic mathematical tool for analyzing the frequency domain content of 2D discrete-space images. However, it has a major drawback for digital image processing applications: the DSFT of a discrete-space image is continuous in the frequency coordinates; there are uncountably infinite numbers of values to compute. As such, discrete (digital) processing or display in the frequency domain is not possible using the DSFT unless it is modified in some way. Fortunately, this is possible when the image is of finite dimensions.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
