2D Fourier Transform

Overview

• Signals as functions (1D, 2D)– Tools

• 1D Fourier Transform– Summary of definition and properties in the different cases

• CTFT, CTFS, DTFS, DTFT• DFT

• 2D Fourier Transforms– Generalities and intuition– Examples– A bit of theory

• Discrete Fourier Transform (DFT)

• Discrete Cosine Transform (DCT)

Signals as functions

1. Continuous functions of real independent variables– 1D: f=f(x)– 2D: f=f(x,y) x,y– Real world signals (audio, ECG, images)

2. Real valued functions of discrete variables– 1D: f=f[k]– 2D: f=f[i,j]– Sampled signals

3. Discrete functions of discrete variables– 1D: y=y[k]– 2D: y=y[i,j]– Sampled and quantized signals– For ease of notations, we will use the same notations for 2 and 3

Images as functions

• Gray scale images: 2D functions– Domain of the functions: set of (x,y) values for which f(x,y) is defined : 2D lattice

[i,j] defining the pixel locations– Set of values taken by the function : gray levels

• Digital images can be seen as functions defined over a discrete domain i,j: 0<i<I, 0<j<J

– I,J: number of rows (columns) of the matrix corresponding to the image– f=f[i,j]: gray level in position [i,j]

Example 1: δ function

[ ]⎩⎨⎧

≠≠==

=jiji

jiji

;0,001

,δ

[ ]⎩⎨⎧ ==

=−otherwise

JjiJji

0;01

,δ

Example 2: Gaussian

2

22

2

21),( σ

πσ

yx

eyxf+

=

2

22

2

21],[ σ

πσ

ji

ejif+

=

Continuous function

Discrete version

Example 3: Natural image

Example 3: Natural image

Fourier Transform

• Different formulations for the different classes of signals– Summary table: Fourier transforms with various combinations of

continuous/discrete time and frequency variables.– Notations:

• CTFT: continuous time FT• DTFT: Discrete Time FT• CTFS: CT Fourier Series (summation synthesis)• DTFS: DT Fourier Series (summation synthesis)• P: periodical signals• T: sampling period• ωs: sampling frequency (ωs=2π/T)• For DTFT: T=1 → ωs=2π

1D FT: basics

Fourier Transform: Concept

A signal can be represented as a weighted sum of sinusoids.

Fourier Transform is a change of basis, where the basis functions consist of sines and cosines (complex exponentials).

Fourier Transform

• Cosine/sine signals are easy to define and interpret.

• However, it turns out that the analysis and manipulation of sinusoidal signals is greatly simplified by dealing with related signals called complex exponential signals.

• A complex number has real and imaginary parts: z = x+j y

• A complex exponential signal:

( )e cos sinjr r jα α α= +

Overview

Self dualD

P

D

P

Discrete Time Fourier Series (DTFS)

Dual with CTFS

C

P

DDiscrete Time Fourier Transform (DTFT)

Dual with DTFT

DC

P

(Continuous Time) Fourier Series (CTFS)

Self-dualCC(Continuous Time) Fourier Transform (CTFT)

DualityAnalysis/SynthesisFrequencyTimeTransform

( ) ( )

1( ) ( )2

j t

tj t

F f t e dt

f t F e d

ω

ω

ω

ω

ω ωπ

−=

=

∫

∫/ 2

2 /

/ 2

2 /

1[ ] ( )

( ) [ ]

Tj kt T

T

j kt T

k

F k f t e dtT

f t F k e

π

π

−

−

=

=

∫

∑

( )

( )

2 /

/ 22 /

/ 2

[ ]

1[ ]

s

s

s

s

j nj t

n

j nj t

s

F e f n e

f n F e e d

πω ωω

ωπω ωω

ω

ωω

−

−

=

=

∑

∫

12 /

01

2 /

0

1[ ] [ ]

[ ] [ ]

Nj kn T

nN

j kn T

n

F k f n eN

f n F k e

π

π

−−

=−

=

=

=

∑

∑

Dualities

FOURIER DOMAINSIGNAL DOMAIN

Sampling Periodicity

SamplingPeriodicity

DTFT

CTFS

Sampling+Periodicity Sampling +PeriodicityDTFS/DFT

Discrete time signals• Sequences of samples

• f[k]: sample values

• Assumes a unitary spacing among samples (Ts=1)

• Normalized frequency Ω

• Transform– DTFT for NON periodic sequences– CTFS for periodic sequences– DFT for periodized sequences

• All transforms are 2π periodic

• Sampled signals

• f(kTs): sample values

• The sampling interval (or period) is Ts

• Non normalized frequency ω

• Transform– DTFT– CSTF– DFT– BUT accounting for the fact that the

sequence values have been generated by sampling a real signal → fk=f(kTs)

• All transforms are periodic with period ωs

sTωΩ =

CTFT

• Continuous Time Fourier Transform

• Continuous time a-periodic signal

• Both time (space) and frequency are continuous variables– NON normalized frequency ω is used

• Fourier integral can be regarded as a Fourier series with fundamental frequency approaching zero

• Fourier spectra are continuous– A signal is represented as a sum of sinusoids (or exponentials) of all

frequencies over a continuous frequency interval

( ) ( )

1( ) ( )2

j t

tj t

F f t e dt

f t F e d

ω

ω

ω

ω

ω ωπ

−=

=

∫

∫

analysis

synthesis

Fourier integral

CTFT: change of notations

Fourier Transform of a 1D continuous signal

( ) ( ) j xF f x e dxωω∞

−

−∞

= ∫

Inverse Fourier Transform1( ) ( )

2j xf x F e dωω ω

π

∞

−∞

= ∫

( ) ( )cos sinj xe x j xω ω ω− = −“Euler’s formula”

Change of notations:

222

x

y

uuv

ω πω πω π

→

→⎧⎨ →⎩

Then CTFT becomes

Fourier Transform of a 1D continuous signal

2( ) ( ) j uxF u f x e dxπ∞

−

−∞

= ∫

Inverse Fourier Transform

2( ) ( ) j uxf x F u e duπ∞

−∞

= ∫

( ) ( )2 cos 2 sin 2j uxe ux j uxπ π π− = −“Euler’s formula”

CTFS

• Continuous Time Fourier Series

• Continuous time periodic signals– The signal is periodic with period T0

– The transform is “sampled” (it is a series)

/ 22 /

/ 2

2 /

1[ ] ( )

( ) [ ]

Tj kt T

T

j kt T

k

F k f t e dtT

f t F k e

π

π

−

−

=

=

∫

∑

0

0

00

0

/ 2

0 / 2

00

1 ( )

( )

2

o

Tjn t

n TT

jn tT n

n

D f t e dtT

f t D e

T

ω

ω

πω

−

−

=

=

=

∫∑

our notations table notations

fundamental frequencyT0↔TDn ↔F[k]

CTFS

• Representation of a continuous time signal as a sum of orthogonal components in a complete orthogonal signal space

– The exponentials are the basis functions

• Fourier series are periodic with period equal to the fundamental in the set (2π/T0)

• Properties– even symmetry → only cosinusoidal components– odd symmetry → only sinusoidal components

CTFS: example 1

CTFS: example 2

From sequences to discrete time signals

• Looking at the sequence as to a set of samples obtained by sampling a real signal with frequency ωs we can still use the formulas for calculating the transforms as derived for the sequences by

– Stratching the time axis (and thus squeezing the frequency axis if Ts>1)

– Enclosing the sampling interval Ts in the value of the sequence samples (DFT)

( )k s sf T f kT=

22

s

ss

T

T

ωππ ω

Ω =

→ =

normalized frequency(sample series) frequency (sampled signal)

spectral periodicity in Ω spectral periodicity in ω

DTFT

• Discrete Time Fourier Transform

• Discrete time a-periodic signal

• The transform is periodic and continuous with period

( )

( )

2 /

/ 22 /

/ 2

[ ]

1[ ]

s

s

s

s

j nj

n

j nj t

s

F e f n e

f n F e e d

πω ωω

ωπω ωω

ω

ωω

−

−

=

=

∑

∫( )2

( ) [ ]

1[ ]2

j k

k

j k

F f k e

f k F e dππ

+∞− Ω

=−∞

Ω

Ω =

= Ω Ω

∑

∫

0 2πΩ =our notations table notations

( ) cs

F FT⎛ ⎞Ω

Ω = ⎜ ⎟⎝ ⎠normalized

frequencynon normalizedfrequencysTωΩ =

2 /s sT π ω= 2 /s sT π ω=

Discrete Time Fourier Transform (DTFT)

• F(Ω) can be obtained from Fc(ω) by replacing ω with Ω/Ts. Thus F(Ω) is identical to Fc(ω) frequency scaled by a factor 1/Ts

– Ts is the sampling interval in time domain

• Notations

( )

( ) ( )

( ) 2 /

2 2

( ) 2 /

( ) [ ] ( ) [ ] s

cs

s ss s

ss

s s

j kj ks

k k

F FT

TT

TT

F F T F

F f k e F T F f k e πω ω

π πωω

ω ω

ω πω ω

ω ω+∞ +∞

−− Ω

=−∞ =−∞

⎛ ⎞ΩΩ = ⎜ ⎟

⎝ ⎠

= → =

Ω= →Ω =

Ω → =

Ω = → = =∑ ∑

__

periodicity of the spectrum

normalized frequency (the spectrum is 2π-periodic)

DTFT: unitary frequency( )

2

12

2 2

12 12

2

12

2

12

2 2

( ) [ ] ( ) [ ]

1[ ] ( ) [ ] ( ) ( )2

( ) [ ]

[ ] ( )

j k j ku

k k

j k j ku j ku

j ku

k

j ku

u f

F f k e F u f k e

f k F e d f k F u e du F u e du

F u f k e

f k F u e du

π

π π

π

π

π

π ω π

π

∞ ∞− Ω −

=−∞ =−∞

Ω

−

∞−

=−∞

−

Ω = =

Ω = → =

= Ω Ω→ = =

⎧ =⎪⎪⎪⎨⎪ =⎪⎪⎩

∑ ∑

∫ ∫ ∫

∑

∫

NOTE: when Ts=1, Ω=ω and the spectrum is 2π-periodic. The unitary frequency u=2π/ Ωcorresponds to the signal frequency f=2π/ω. This could give a better intuition of the transform properties.

Connection DTFT-CTFT

0 Ts 4Ts

0 1 4

t

t

k

ω

ω

Ω

0

0

0

2π/Ts

2π

Fc(ω)

F(Ω)

sampling periodization

f(t)

f(kTs)Fc(ω)_

f[k]

Differences DTFT-CTFT

• The DTFT is periodic with period Ωs=2π (or ωs=2π/Ts)

• The discrete-time exponential ejΩk has a unique waveform only for values of Ω in a continuous interval of 2π

• Numerical computations can be conveniently performed with the Discrete Fourier Transform (DFT)

DTFS

• Discrete Time Fourier Series

• Discrete time periodic sequences of period N0– Fundamental frequency

0 02 / NπΩ =

12 /

01

2 /

0

1[ ] [ ]

[ ] [ ]

Nj kn T

nN

j kn T

n

F k f n eN

f k F k e

π

π

−−

=−

=

=

=

∑

∑

00

00

1

00

1

0

1 [ ]

[ ]

Njr k

rk

Njr k

rr

D f k eN

f k D e

−− Ω

=

−Ω

=

=

=

∑

∑

our notations table notations

Discrete Fourier Transform (DFT)

• The DFT transforms N0 samples of a discrete-time signal to the same number of discrete frequency samples

• The DFT and IDFT are a self-contained, one-to-one transform pair for a length-N0discrete-time signal (that is, the DFT is not merely an approximation to the DTFT as discussed next)

• However, the DFT is very often used as a practical approximation to the DTFT

0 00 0

0 00 0

21 1

0 021 1

0 00 0

00

1 1

2

N N j rkjr k N

r k kk k

N N jr kjr k N

k r rk k

F f e f e

f F e F eN N

N

π

π

π

− − −− Ω

= =

− −Ω

= =

= =

= =

Ω =

∑ ∑

∑ ∑

DFT

k

zero padding

0 N0

r0 2π

F(Ω)

4π2π/N0

Discrete Cosine Transform (DCT)

• Operate on finite discrete sequences (as DFT)

• A discrete cosine transform (DCT) expresses a sequence of finitely many data points in terms of a sum of cosine functions oscillating at different frequencies

• DCT is a Fourier-related transform similar to the DFT but using only real numbers

• DCT is equivalent to DFT of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), where in some variants the input and/or output data are shifted by half a sample

• There are eight standard DCT variants, of which four are common

• Strong connection with the Karunen-Loeven transform– VERY important for signal compression

DCT

• DCT implies different boundary conditions than the DFT or other related transforms

• A DCT, like a cosine transform, implies an even periodic extension of the original function

• Tricky part– First, one has to specify whether the function is even or odd at both the left and

right boundaries of the domain – Second, one has to specify around what point the function is even or odd

• In particular, consider a sequence abcd of four equally spaced data points, and say that we specify an even left boundary. There are two sensible possibilities: either the data is even about the sample a, in which case the even extension is dcbabcd, or the data is even about the point halfway between a and the previous point, in which case the even extension is dcbaabcd (a is repeated).

Symmetries

DCT

0

0

1

00 0

1

000 0

1cos 0,...., 12

2 1 1cos2 2

N

k nn

N

n kk

X x n k k NN

kx X X kN N

π

π

−

=

−

=

⎡ ⎤⎛ ⎞= + = −⎜ ⎟⎢ ⎥⎝ ⎠⎣ ⎦⎧ ⎫⎡ ⎤⎛ ⎞= + +⎨ ⎬⎜ ⎟⎢ ⎥⎝ ⎠⎣ ⎦⎩ ⎭

∑

∑

• Warning: the normalization factor in front of these transform definitions is merely a convention and differs between treatments.

– Some authors multiply the transforms by (2/N0)1/2 so that the inverse does not require any additional multiplicative factor.

• Combined with appropriate factors of √2 (see above), this can be used to make the transform matrix orthogonal.

Sinusoids

• Frequency domain characterization of signals

Frequency domain

Signal domain

( ) ( ) j tF f t e dtωω+∞

−

−∞

= ∫

Gaussian

rect

sinc function

Images vs Signals

1D

• Signals

• Frequency– Temporal– Spatial

• Time (space) frequency characterization of signals

• Reference space for– Filtering– Changing the sampling rate– Signal analysis– ….

2D

• Images

• Frequency– Spatial

• Space/frequency characterization of 2D signals

• Reference space for– Filtering– Up/Down sampling– Image analysis– Feature extraction– Compression– ….

2D spatial frequencies

• 2D spatial frequencies characterize the image spatial changes in the horizontal (x) and vertical (y) directions

– Smooth variations -> low frequencies– Sharp variations -> high frequencies

x

y

ωx=1ωy=0 ωx=0

ωy=1

2D Frequency domain

ωx

ωy

Large vertical frequencies correspond to horizontal lines

Large horizontal frequencies correspond to vertical lines

Small horizontal and vertical frequencies correspond smooth grayscale changes in both directions

Large horizontal and vertical frequencies correspond sharp grayscale changes in both directions

Vertical grating

ωx

ωy

0

Double grating

ωx

ωy

0

Smooth rings

ωx

ωy

2D box2D sinc

Margherita Hack

log amplitude of the spectrum

Einstein

log amplitude of the spectrum

What we are going to analyze

• 2D Fourier Transform of continuous signals (2D-CTFT)

• 2D Fourier Transform of discrete space signals (2D-DTFT)

• 2D Discrete Fourier Transform (2D-DFT)

( ) ( ) , ( ) ( )j t j tF f t e dt f t F e dtω ωω ω+∞ +∞

−

−∞ −∞

= =∫ ∫

0 00 0

0

1 1

00 00 0

1 2[ ] , [ ] ,N N

jr k jr kr N r

k rF f k e f k F e

N Nπ− −

− Ω Ω

= =

= = Ω =∑ ∑

2

1( ) [ ] , [ ] ( )2

j k j k

kF f k e f k F e dt

ππ

∞− Ω Ω

=−∞

Ω = = Ω∑ ∫

1D

1D

1D

2D Continuous Fourier Transform

• Continuous case (x and y are real) – 2D-CTFT (notation 1)

( ) ( ) ( )

( ) ( ) ( )2

ˆ , ,

1 ˆ, ,4

x y

x y

j x yx y

j x yx y x y

f f x y e dxdy

f x y f e d d

ω ω

ω ω

ω ω

ω ω ω ωπ

+∞− +

−∞

+∞+

−∞

=

=

∫

∫

( ) ( ) ( ) ( )

( ) ( )

* *2

222

1 ˆ, , , ,ˆ4

1 ˆ, ,4

x y x y x y

x y x y

f x y g x y dxdy f g d d

f g f x y dxdy f d d

ω ω ω ω ω ωπ

ω ω ω ωπ

=

= → =

∫∫ ∫∫

∫∫ ∫∫

Parseval formula

Plancherel equality

2D Continuous Fourier Transform

• Continuous case (x and y are real) – 2D-CTFT

( ) ( ) ( )

( ) ( ) ( ) ( )

( ) ( ) ( )

2

222

222

22

ˆ , ,

1 ˆ, , 24

1 ˆ , 24

x

y

j ux vy

j ux vy

j ux vy

uv

f u v f x y e dxdy

f x y f u v e dudv

f u v e dudv

π

π

π

ω πω π

ππ

ππ

+∞− +

−∞

+∞+

−∞

+∞+

−∞

=

=

=

= =

=

∫

∫

∫

2D Continuous Fourier Transform

• 2D Continuous Fourier Transform (notation 2)

( ) ( ) ( )

( ) ( ) ( )

2

2

ˆ , ,

ˆ, ,

j ux vy

j ux vy

f u v f x y e dxdy

f x y f u v e dudv

π

π

+∞− +

−∞

+∞+

−∞

=

= =

∫

∫

22 ˆ( , ) ( , )f x y dxdy f u v dudv∞ ∞ ∞ ∞

−∞ −∞ −∞ −∞

=∫ ∫ ∫ ∫ Plancherel’s equality

2D Discrete Fourier Transform

00

00

0

1

0

1

00

00

[ ]

1[ ]

2

Njr k

rk

Njr k

N rr

F f k e

f k F eN

Nπ

−− Ω

=

−Ω

=

=

=

Ω =

∑

∑

0 00

0 00

0

1 1( )

0 0

1 1( )

20 00

00

[ , ] [ , ]

1[ , ] [ , ]

2

N Nj ui vk

i k

N Nj ui vk

Nu v

F u v f i k e

f i k F u v eN

Nπ

− −− Ω +

= =

− −Ω +

= =

=

=

Ω =

∑ ∑

∑ ∑

The independent variable (t,x,y) is discrete

Delta

• Sampling property of the 2D-delta function (Dirac’s delta)

• Transform of the delta function

0 0 0 0( , ) ( , ) ( , )x x y y f x y dxdy f x yδ∞

−∞

− − =∫

2 ( )( , ) ( , ) 1j ux vyF x y x y e dxdyπδ δ∞ ∞

− +

−∞ −∞

= =∫ ∫

0 02 ( )2 ( )0 0 0 0( , ) ( , ) j ux vyj ux vyF x x y y x x y y e dxdy e ππδ δ

∞ ∞− +− +

−∞ −∞

− − = − − =∫ ∫ shifting property

Constant functions

• Inverse transform of the impulse function

• Fourier Transform of the constant (=1 for all x and y)

2 ( )

( , ) 1 ,

( , )j ux vy

k x y x y

F k e dxdy u vπ δ∞ ∞

− +

−∞ −∞

= ∀

= =∫ ∫

1 2 ( ) 2 (0 0)( , ) ( , ) 1j ux vy j x vF u v u v e dudv eπ πδ δ∞ ∞

− + +

−∞ −∞

= = =∫ ∫

Trigonometric functions

• Cosine function oscillating along the x axis– Constant along the y axis

2 ( )

2 ( ) 2 ( )2 ( )

( , ) cos(2 )

cos(2 ) cos(2 )

2

j ux vy

j fx j fxj ux vy

s x y fx

F fx fx e dxdy

e e e dxdy

π

π ππ

π

π π∞ ∞

− +

−∞ −∞

∞ ∞ −− +

−∞ −∞

=

= =

⎡ ⎤+= ⎢ ⎥

⎣ ⎦

∫ ∫

∫ ∫

[ ]

2 ( ) 2 ( ) 2

2 2 ( ) 2 ( ) 2 ( ) 2 ( )

12

1 112 2

1 ( ) ( )2

j u f x j u f x j vy

j vy j u f x j u f x j u f x j u f x

e e e dxdy

e dy e e dx e e dx

u f u f

π π π

π π π π π

δ δ

∞ ∞− − − + −

−∞ −∞

∞ ∞ ∞− − − − + − − − +

−∞ −∞ −∞

⎡ ⎤= + =⎣ ⎦

⎡ ⎤ ⎡ ⎤= + = + =⎣ ⎦ ⎣ ⎦

− + +

∫ ∫

∫ ∫ ∫

Vertical grating

ωx

ωy

0-2πf 2πf

Ex. 1

Ex. 2

Ex. 3

Magnitudes

Examples

Properties

Linearity

Shifting

Modulation

Convolution

Multiplication

Separability

( , ) ( , ) ( , ) ( , )af x y bg x y aF u v bG u v+ ⇔ +

( , )* ( , ) ( , ) ( , )f x y g x y F u v G u v⇔

( , ) ( , ) ( , )* ( , )f x y g x y F u v G u v⇔

( , ) ( ) ( ) ( , ) ( ) ( )f x y f x f y F u v F u F v= ⇔ =

0 02 ( )0 0( , ) ( , )j ux vyf x x y x e F u vπ− +− − ⇔

0 02 ( )0 0( , ) ( , )j u x v ye f x y F u u v vπ + ⇔ − −

Separability

1. Separability of the 2D Fourier transform– 2D Fourier Transforms can be implemented as a sequence of 1D Fourier

Transform operations performed independently along the two axis

( )

2 ( )

2 2 2 2

2

( , ) ( , )

( , ) ( , )

( , ) ,

j ux vy

j ux j vy j vy j ux

j vy

F u v f x y e dxdy

f x y e e dxdy e dy f x y e dx

F u y e dy F u v

π

π π π π

π

∞ ∞− +

−∞ −∞

∞ ∞ ∞ ∞− − − −

−∞ −∞ −∞ −∞

∞−

−∞

= =

= =

= =

∫ ∫

∫ ∫ ∫ ∫

∫

2D DFT 1D DFT along the rows

1D DFT along the cols

Separability

• Separable functions can be written as

2. The FT of a separable function is the product of the FTs of the two functions

( ) ( )

( ) ( )

2 ( )

2 2 2 2

( , ) ( , )

( ) ( )

j ux vy

j ux j vy j vy j ux

F u v f x y e dxdy

h x g y e e dxdy g y e dy h x e dx

H u G v

π

π π π π

∞ ∞− +

−∞ −∞

∞ ∞ ∞ ∞− − − −

−∞ −∞ −∞ −∞

= =

= =

=

∫ ∫

∫ ∫ ∫ ∫

( ) ( ) ( ) ( ) ( ) ( ), ,f x y h x g y F u v H u G v= ⇒ =

( ) ( ) ( ),f x y f x g y=

• Fourier Transform of a 2D a-periodic signal defined over a 2D discrete grid– The grid can be thought of as a 2D brush used for sampling the continuous

signal with a given spatial resolution (Tx,Ty)

2D Fourier Transform of a Discrete function

2

1( ) [ ] , [ ] ( )2

j k j k

kF f k e f k F e dt

ππ

∞− Ω Ω

=−∞

Ω = = Ω∑ ∫

( )

( )

1 2

1 2

1 2

1 2

22 2

( , ) [ , ]

1[ ] ( , )4

x y

x y

j k k

x yk k

j k kx y x y

F f k k e

f k F e dπ ππ

− Ω + Ω+∞ +∞

=−∞ =−∞

Ω + Ω

Ω Ω =

= Ω Ω Ω Ω

∑ ∑

∫ ∫

1D

2D

Ωx,Ωy: normalized frequency

Unitary frequency notations

1 2

1 2

1 2

2 ( )1 2

1/ 2 1/ 22 ( )

1 21/ 2 1/ 2

22

( , ) [ , ]

[ , ] ( , )

x

y

j k u k v

k k

j k u k v

uv

F u v f k k e

f k k F u v e dudv

π

π

ππ

+∞ +∞− +

=−∞ =−∞

− +

− −

Ω =⎧⎨Ω =⎩

=

=

∑ ∑

∫ ∫

• The integration interval for the inverse transform has width=1 instead of 2π– It is quite common to choose

1 1,2 2

u v−≤ <

Properties

• Periodicity: 2D Fourier Transform of a discrete a-periodic signal is periodic– The period is 1 for the unitary frequency notations and 2π for normalized

frequency notations. – Proof (referring to the firsts case)

( )2 ( ) ( )( , ) [ , ] j u k m v l n

m nF u k v l f m n e π

∞ ∞− + + +

=−∞ =−∞

+ + = ∑ ∑

( )2 2 2[ , ] j um vn j km j ln

m nf m n e e eπ π π

∞ ∞− + − −

=−∞ =−∞

= ∑ ∑

2 ( )[ , ] j um vn

m nf m n e π

∞ ∞− +

=−∞ =−∞

= ∑ ∑

1 1

( , )F u v=

Arbitrary integers

Properties

• Linearity

• shifting

• modulation

• convolution

• multiplication

• separability

• energy conservation properties also exist for the 2D Fourier Transform of discrete signals.

• NOTE: in what follows, (k1,k2) is replaced by (m,n)

2D DTFT Properties

Linearity

Shifting

Modulation

Convolution

Multiplication

Separable functions

Energy conservation

[ , ] [ , ] ( , ) ( , )af m n bg m n aF u v bG u v+ ⇔ +

0 02 ( )0 0[ , ] ( , )j um vnf m m n n e F u vπ− +− − ⇔

[ , ] [ , ] ( , )* ( , )f m n g m n F u v G u v⇔

[ , ]* [ , ] ( , ) ( , )f m n g m n F u v G u v⇔

0 02 ( )0 0[ , ] ( , )j u m v ne f m n F u u v vπ + ⇔ − −

[ , ] [ ] [ ] ( , ) ( ) ( )f m n f m f n F u v F u F v= ⇔ =1/ 2 1/ 2

2 2

1/ 2 1/ 2

[ , ] ( , )m n

f m n F u v dudv∞ ∞

=−∞ =−∞ − −

=∑ ∑ ∫ ∫

Impulse Train

Define a comb function (impulse train) as follows

, [ , ] [ , ]M Nk l

comb m n m kM n lNδ∞ ∞

=−∞ =−∞

= − −∑ ∑

where M and N are integers

2[ ]comb n

n

1

Appendix

2D-DTFT: delta

Define Kronecker delta function

DT Fourier Transform of the Kronecker delta function

1, for 0 and 0[ , ]

0, otherwisem n

m nδ= =⎧ ⎫

= ⎨ ⎬⎩ ⎭

( ) ( )2 2 0 0( , ) [ , ] 1j um vn j u v

m n

F u v m n e eπ πδ∞ ∞

− + − +

=−∞ =−∞

⎡ ⎤= = =⎣ ⎦∑ ∑

2D DT Fourier Transform: constant

Fourier Transform of 1

To prove: Take the inverse Fourier Transform of the Dirac delta function and use the fact that the Fourier Transform has to be periodic with period 1.

( )2

[ , ] 1, ,

[ , ] 1

( , )

j uk vl

k l

k l

f k l k l

F u v e

u k v l

π

δ

∞ ∞− +

=−∞ =−∞

∞ ∞

=−∞ =−∞

= ∀

⎡ ⎤= =⎣ ⎦

= − −

∑ ∑

∑ ∑ periodic with period 1 along u and v