## 8. Explain about Aliasing and Moire patterns.

>Table of contents

**Aliasing and Moiré Patterns:**

Functions whose area under the curve is finite can be represented in terms of sines and cosines of

various frequencies. The sine/cosine component with the highest frequency determines the

highest “frequency content” of the function. Suppose that this highest frequency is finite and that

the function is of unlimited duration (these functions are called band-limited functions).Then, the

Shannon sampling theorem [Brace well (1995)] tells us that, if the function is sampled at a rate

equal to or greater than twice its highest frequency, it is possible to recover completely the

original function from its samples. If the function is undersampled, then a phenomenon called

aliasing corrupts the sampled image. The corruption is in the form of additional frequency

components being introduced into the sampled function. These are called aliased frequencies.

Note that the sampling rate in images is the number of samples taken (in both spatial directions)

per unit distance.

As it turns out, except for a special case discussed in the following paragraph, it is impossible to

satisfy the sampling theorem in practice. We can only work with sampled data that are finite in

duration. We can model the process of converting a function of unlimited duration into a

function of finite duration simply by multiplying the unlimited function by a “gating function”

that is valued 1 for some interval and 0 elsewhere. Unfortunately, this function itself has

frequency components that extend to infinity. Thus, the very act of limiting the duration of a

band-limited function causes it to cease being band limited, which causes it to violate the key

condition of the sampling theorem. The principal approach for reducing the aliasing effects on an

image is to reduce its high-frequency components by blurring the image prior to sampling.

However, aliasing is always present in a sampled image. The effect of aliased frequencies can be

seen under the right conditions in the form of so called Moiré patterns.

There is one special case of significant importance in which a function of infinite duration can be

sampled over a finite interval without violating the sampling theorem. When a function is

periodic, it may be sampled at a rate equal to or exceeding twice its highest frequency and it is

possible to recover the function from its samples provided that the sampling captures exactly an

integer number of periods of the function. This special case allows us to illustrate vividly the

Moiré effect. Figure 8 shows two identical periodic patterns of equally spaced vertical bars,

rotated in opposite directions and then superimposed on each other by multiplying the two

images. A Moiré pattern, caused by a breakup of the periodicity, is seen in Fig.8 as a 2-D

sinusoidal (aliased) waveform (which looks like a corrugated tin roof) running in a vertical

direction. A similar pattern can appear when images are digitized (e.g., scanned) from a printed

page, which consists of periodic ink dots.

Fig.8. Illustration of the Moiré pattern effect

>Table of contents

## 0 comments :

## Post a Comment