# 8. Explain about Aliasing and Moire patterns.

## 8. Explain about Aliasing and Moire patterns.

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