Homomorphic filtering GudangMovies21 Rebahinxxi LK21

Homomorphic filtering is a generalized technique for signal and image processing, involving a nonlinear mapping to a different domain in which linear filter techniques are applied, followed by mapping back to the original domain. This concept was developed in the 1960s by Thomas Stockham, Alan V. Oppenheim, and Ronald W. Schafer at MIT and independently by Bogert, Healy, and Tukey in their study of time series.




Image enhancement


Homomorphic filtering is sometimes used for image enhancement. It simultaneously normalizes the brightness across an image and increases contrast. Here homomorphic filtering is used to remove multiplicative noise. Illumination and reflectance are not separable, but their approximate locations in the frequency domain may be located. Since illumination and reflectance combine multiplicatively, the components are made additive by taking the logarithm of the image intensity, so that these multiplicative components of the image can be separated linearly in the frequency domain. Illumination variations can be thought of as a multiplicative noise, and can be reduced by filtering in the log domain.
To make the illumination of an image more even, the high-frequency components are increased and low-frequency components are decreased, because the high-frequency components are assumed to represent mostly the reflectance in the scene (the amount of light reflected off the object in the scene), whereas the low-frequency components are assumed to represent mostly the illumination in the scene. That is, high-pass filtering is used to suppress low frequencies and amplify high frequencies, in the log-intensity domain.




= Operation

=
Homomorphic filtering can be used for improving the appearance of a grayscale image by simultaneous intensity range compression (illumination) and contrast enhancement (reflection).




m
(
x
,
y
)
=
i
(
x
,
y
)
∙
r
(
x
,
y
)


{\displaystyle m(x,y)=i(x,y)\bullet r(x,y)}


Where,
m = image,
i = illumination,
r = reflectance
We have to transform the equation into frequency domain in order to apply high pass filter. However, it's very difficult to do calculation after applying Fourier transformation to this equation because it's not a product equation anymore. Therefore, we use 'log' to help solve this problem.




ln
⁡
(
m
(
x
,
y
)
)
=
ln
⁡
(
i
(
x
,
y
)
)
+
ln
⁡
(
r
(
x
,
y
)
)


{\displaystyle \ln(m(x,y))=\ln(i(x,y))+\ln(r(x,y))}


Then, applying Fourier transformation




ϝ
(
l
n
(
m
(
x
,
y
)
)
)
=
ϝ
(
l
n
(
i
(
x
,
y
)
)
)
+
ϝ
(
l
n
(
r
(
x
,
y
)
)
)


{\displaystyle \digamma (ln(m(x,y)))=\digamma (ln(i(x,y)))+\digamma (ln(r(x,y)))}


Or



M
(
u
,
v
)
=
I
(
u
,
v
)
+
R
(
u
,
v
)


{\displaystyle M(u,v)=I(u,v)+R(u,v)}


Next, applying high-pass filter to the image. To make the illumination of an image more even, the high-frequency components are increased and low-frequency components are decrease.




N
(
u
,
v
)
=
H
(
u
,
v
)
∙
M
(
u
,
v
)


{\displaystyle N(u,v)=H(u,v)\bullet M(u,v)}


Where
H = any high-pass filter
N = filtered image in frequency domain
Afterward, returning frequency domain back to the spatial domain by using inverse Fourier transform.




n
(
x
,
y
)
=
i
n
v
F
(
N
(
u
,
v
)
)


{\displaystyle n(x,y)=invF(N(u,v))}


Finally, using the exponential function to eliminate the log we used at the beginning to get the enhanced image




n
e
w
I
m
a
g
e
(
x
,
y
)
=
e
x
p
(
n
(
x
,
y
)
)


{\displaystyle newImage(x,y)=exp(n(x,y))}


The following figures show the results of applying the homomorphic filter, high-pass filter, and the both homomorphic and high-pass filter. All figures were produced using Matlab.

According to figures one to four, we can see how homomorphic filtering is used for correcting non-uniform illumination in the image, and the image become clearer than the original. On the other hand, if we apply the high pass filter to the homomorphic filtered image, the edges of the images become sharper and the other areas become dimmer. This result is similar to applying only a high-pass filter to the original image.




Anti-homomorphic filtering


It has been suggested that many cameras already have an approximately logarithmic response function (or more generally, a response function which tends to compress dynamic range), and display media such as television displays, photographic print media, etc., have an approximately anti-logarithmic response, or an otherwise dynamic range expansive response. Thus homomorphic filtering happens accidentally (unintentionally) whenever we process pixel values f(q) on the true quantigraphic unit of light q. Therefore it has been proposed that another useful kind of filtering is anti-homomorphic filtering in which images f(q) are first dynamic-range expanded to recover the true light q, upon which linear filtering is performed, followed by dynamic range compression back into image space for display.




Audio and speech analysis


Homomorphic filtering is used in the log-spectral domain to separate filter effects from excitation effects, for example in the computation of the cepstrum as a sound representation; enhancements in the log spectral domain can improve sound intelligibility, for example in hearing aids.




Surface electromyography signals (sEMG)


Homomorphic filtering has been used to remove the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of the sEMG signal itself. In this way, only information about motor unit action potential (MUAP) shape and amplitude was maintained; this was then used to estimate the parameters of a time-domain model of the MUAP itself.




Neural decoding


How individual neurons or networks encode information is the subject of numerous studies and research. In central nervous system it mainly happens by altering the spike firing rate (frequency encoding) or relative spike timing (time encoding).
Time encoding consists of altering the random inter-spikes intervals (ISI) of the stochastic impulse train in output from a neuron. Homomorphic filtering was used in this latter case to obtain ISI variations from the power spectrum of the spike train in output from a neuron with or without the use of neuronal spontaneous activity. The ISI variations were caused by an input sinusoidal signal of unknown frequency and small amplitude, i.e. not sufficient, in absence of noise to excite the firing state. The frequency of the sinusoidal signal was recovered by using homomorphic filtering based procedures.




See also


Digital image processing
Image processing
Digital imaging
Image editing




References






Further reading


A.V. Oppenheim, R.W. Schafer, T.G. Stockham "Nonlinear Filtering of Multiplied and Convolved Signals" Proceedings of the IEEE Volume 56 No. 8 August 1968 pages 1264-1291




External links


Overview of homomorphic filtering

Kata Kunci Pencarian:

• Homomorphic filtering
• Image restoration by artificial intelligence
• Digital image processing
• Image editing
• Cepstrum
• Variety (universal algebra)
• Mark Ludwig
• Shadow and highlight enhancement
• Keshab K. Parhi
• Western clawed frog

GitHub - LazyRacc00n/Homomorphic_Filtering: Homomorphic filtering

Homomorphic filtering | PPT

Homomorphic filtering | PPT

Homomorphic filtering | PPT

GitHub - Eben2020-hp/Homomorphic-Filtering: Implementation of ...

GitHub - Eben2020-hp/Homomorphic-Filtering: Implementation of ...

Homomorphic filtering | Semantic Scholar

Homomorphic filtering | Semantic Scholar

Homomorphic filtering

Download PDF - Homomorphic Filtering [dmqe6me8ex05]

Homomorphic filtering

Process of Homomorphic filtering | Download Scientific Diagram

homomorphic filtering

Daftar Isi

• Homomorphic filtering - Wikipedia
• Homomorphic filtering - Steve on Image Processing with MATLAB
• HOMOMORPHIC FILTERING - BITS Pilani
• HOMOMORPHIC FILTERS - Springer
• Homomorphic Filters - SpringerLink
• Homomorphic filtering - File Exchange - MATLAB Central
• Homomorphic filtering | PPT - SlideShare
• The Concept of Homomorphic Filtering - Stack Overflow
• Homomorphic filtering for the image enhancement based on …
• Contrast Enhancement in Images by Homomorphic Filtering and …

Homomorphic filtering - Wikipedia

Homomorphic filtering is a generalized technique for signal and image processing, involving a nonlinear mapping to a different domain in which linear filter techniques are applied, followed by mapping back to the original domain.

Homomorphic filtering - Steve on Image Processing with MATLAB

Jun 25, 2013 · In homomorphic filtering we first transform the multiplicative components to additive components by moving to the log domain. Then we use a high-pass filter in the log domain to remove the low-frequency illumination component while …

HOMOMORPHIC FILTERING - BITS Pilani

HOMOMORPHIC FILTERING An image f(x,y) can be represented as f(x,y)= i(x,y) r(x,y) Incident Reflectance If you want to see frequency components of both reflectance and illumination component then it is not possible through Fourier Transformation { f ( x, y)} z { i ( x, y)}. { r ( x, y)}

HOMOMORPHIC FILTERS - Springer

Homomorphic filtering has found various practical applications, e.g., in satellite image processing and in the identification of fuzzy fingerprints. Homomorphic filtering has also found several applications in other areas, e.g., in speech processing and in geophysical signal processing.

Homomorphic Filters - SpringerLink

Homomorphic filtering has found various practical applications, e.g., in satellite image processing and in the identification of fuzzy fingerprints. Homomorphie filtering has also found several applications in other areas, e.g., in speech processing and in geophysical signal processing.

Homomorphic filtering - File Exchange - MATLAB Central

Sep 8, 2008 · MATLAB code for beginners that performs Homomorphic filtering, Using Butterworth High Pass Filter for performing filtering. Copy the files in your work space Type "ho_filter" to see the results

Homomorphic filtering | PPT - SlideShare

Oct 12, 2019 · Homomorphic filtering is a technique used to remove multiplicative noise from images by transforming the image into the logarithmic domain, where the multiplicative components become additive.

The Concept of Homomorphic Filtering - Stack Overflow

Dec 14, 2015 · 2- According to this source, Homomorphic filtering is a frequency domain filtering process that compresses the brightness (from the lighting condition) while enhancing the contrast (from the reflectance properties of the object).

Homomorphic filtering for the image enhancement based on …

Mar 1, 2023 · Homomorphic filtering is used to attenuate the contribution made by the illumination and amplify the reflectance components of an image. This work uses a fractional-order derivative to enhance the mid- and high-frequencies and preserve the low-frequencies.

Contrast Enhancement in Images by Homomorphic Filtering and …

Homomorphic filtering (HF) is a methodology that separates an image into two components: illumination and reflectance. Through the processing of these components, it is possible to significantly improve the contrast of the low-frequency components while preserving the edges and sharp features of the image.