Diffraction, Fourier Optics and Imaging

Chapter 2 - Linear Systems and Transforms

2.1   INTRODUCTION

Diffraction as well as imaging can often be modeled as linear systems. First of all, a
system is an input–output mapping. Thus, given an input, the system generates an
output. For example, in a diffraction or imaging problem, the input and output are
typically a wave at an input plane and the corresponding diffracted wave at a
distance from the input plane.

Optical systems are quite analogous to communication systems. Both types of
systems have a primary purpose of collecting and processing information. Speech
signals processed by communication systems are 1-D whereas images are 2-D.
One-dimensional signals are typically temporal whereas 2-D signals are typically
spatial. For example, an optical system utilizing a laser beam has spatial coherence.
Then, the signals can be characterized as 2-D or 3-D complex-valued field amplitudes.
Spatial coherence is necessary in order to observe diffraction. Illumination such as
ordinary daylight does not have spatial coherence. Then, the signals can be
characterized as 2-D spatial, real-valued intensities.

Linear time-invariant and space-invariant communication and optical systems are
usually analyzed by frequency analysis using the Fourier transform. Nonlinear
optical elements such as the photographic film and nonlinear electronic components
such as diodes have similar input–output characteristics.

In both types of systems, Fourier techniques can be used for system synthesis as
well. An example is two-dimensional filtering. Theoretically optical matched filters,
optical image processing techniques are analogous to matched filters and image
processing techniques used in communications and signal processing.

In this chapter, linear system theory and Fourier transform theory as related
especially to diffraction, optical imaging, and related areas are discussed. The
chapter consists of eight sections. The properties of linear systems with emphasis on
convolution and shift invariance are highlighted in Section 2.2. The 1-D Fourier
transform and the continuous-space Fourier transform (simply called the Fourier
transform (FT) in the rest of the book) are introduced in Section 2.3. The conditions
for the existence of the Fourier transform are given in Section 2.4. The properties of
the Fourier transform are summarized in Section 2.5.

The Fourier transform discussed so far has a complex exponential kernel. It is
actually possible to define the Fourier transform as a real transform with cosine and
sine kernel functions. The resulting real Fourier transform is sometimes more useful.
The 1-D real Fourier transform is discussed in Section 2.6. Amplitude and phase
spectra of the 1-D Fourier transform are defined in Section 2.7.

Especially in optics and wave propagation applications, the 2-D signals
sometimes have circular symmetry. In that case, the Fourier transform becomes
the Hankel transform in cylindrical coordinates. The Hankel transform is discussed
in Section 2.8.

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