# Introduction applications where the experts make decision with

Introduction

Image processing is a method to convert an
image into digital form and perform some operations on it, in order to get an
enhanced image or to extract some useful information from it.  Image enhancement plays a fundamental role in
image processing applications where the experts make decision with respect to
the image information. Image enhancement means improvement of an image
appearance by increasing dominance of some features or by decreasing ambiguity
between different regions of the image.

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1.      Image enhancement techniques

Image enhancement techniques can be
classified into two methods:

1.      Spatial domain methods 2.Frequency
domain methods.

Spatial domain method act on pixels directly.
The pixel values are altered to achieve the desired enhancement. Point
processing methods , Gray Level Transformation, log transformation, histogram
processing, Image Negatives ,morphological operators,
Piecewise Linear Transformation,
Power Law Transform, Spatial Filtering  are
spatial domain enhancement methods.

Frequency
domain method is a term used to describe the analysis of mathematical functions
or signals with respect to frequency and operate directly on the image
transform coefficients. Commonly used transform co-efficient are Fourier
Transform(FT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform
(DWT).The basic idea is to enhance the image by manipulating the transform
coefficients. In Frequency Domain method, the image is converted to frequency
domain. Hence the Fourier Transform of the image is computed first. All the
enhancement operations are performed on the Fourier transform of the image and
then the Inverse Fourier transform is performed to get the desired result.

2.
Contrast Enhancement Techniques

Contrast
Enhancement aims at enhancing the global or local contrast of an image. Some of
the enhancement techniques include histogram equalization, Genetic algorithms and
fuzzy set algorithms. Many modifications are done in the standard methods to
improve the contrast of the images.

3.     Literature Survey

An overview of
several contrast enhancement methods is discussed in the literature.

Cheolkon Jung 1
proposed a new contrast enhancement method named Optimized Perceptual Tone
Mapping (OPTM) which focuses on human visual attention. This method is a three
step process. Initially to measure the human visual attention, a saliency
histogram is constructed. Next the contrast enhancement of images subject to
constraints like maximum tone distortion is performed .At the end, to avoid over
enhancement the pixel mapping function is adjusted. The proposed algorithm
achieves improved performance and good results without over enhancement. But
the method requires more time for contrast enhancement compared to Histogram equalization(HE) and  contrast limited histogram equalization (CLAHE)

Anil Singh Parihar 2 proposed a fuzzy
dissimilarity histogram (FDHE) algorithm for contrast enhancement and extended
it to Fuzzy Contextual Contrast Enhancement (FCCE).In order  to
capture intensity level differences in the neighborhood of the pixels
FDHE is used.It provides global contrast enhancement. When the contrast was
increased, the fuzzy theory helped in retaining the continuity in smooth
images. FDHE was extended to FCCE to provide a contextual  intensity transform function. FCCE uses both
global and local enhancement. FDHE and FCCE involve no parameters. Original
shape of the histogram is preserved.

Shilpa Suresh 3 proposed a novel cuckoo
search based enhancement algorithm for the enhancement of satellite images. The
proposed method is implemented in three phases: a chaotic initialization phase,
an adaptive levy flight strategy and mutative randomization phase. The
algorithm improves the convergence rate of the standard CS algorithm and proves
increased adaptability for different images. This method shows little
complexity in execution.

Huanjing Yue 4 proposed to enhance images
by estimating illumination and reflectance layers through intrinsic image
decomposition. The split Bregman algorithm is adopted to solve the
decomposition problem. The Gamma correction is performed after decomposition to
boost the details globally. Later CLAHE is used for the enhancement of local
details.Results show high performance than the other decomposition models. The
proposed method is designed only for contrast enhancement. It cannot be used
for other methods like surface re-texturing, object insertion,  and video enhancement.

Se
EunKim 5 proposed an entropy based contrast enhancement method in the wavelet
domain. Initially it uses a local entropy scaling in the wavelet domain. For
entropy scaling in the wavelet transform domain to enhance image contrast,
mathematical works were used and then a color enhancing method in the HSI color
space was developed. The algorithm worked in two steps: The low frequency
coefficients in the wavelet domain are modified and then the saturation
component of the HIS color space is linearly scaled by using the enhanced
intensity component. By using the proposed method the details and color
information of low light images are good without any post processing.

M.Shakeri 6
proposed a contrast enhancement algorithm based on local histogram equalization
which was used for automatic determination of the number of sub-histograms and
density based histogram division. The algorithm worked in three stages. Initially,
the estimation of the number of clusters for
image brightness levels is done using histogram equalization. In the next
stage,  the image brightness levels are
clustered and finally include the
contrast enhancement for each individual cluster separately. The
algorithm is compared with other methods based on quality and quantity
measurement.

Lalit Maurya 7 proposed
a social spider optimization algorithm which produces two enhanced images one
with high contrast, increased entropy and the other image with increased peak
signal to noise ratio. Later the two enhanced images are combined to get an
effective image. Comparisons were done with HE, Linear contrast stretching,
Standard Particle Swarm Optimization. Results show that the proposed method
achieves high PSNR, brightness preservation and contrast enhancement of any
given input image which leads to better visual quality

Jeyong
Shin 8 proposed histogram-based locality- preserving CE (HBLPCE), an
optimization problem to preserve the localities of the histogram for performing
contrast enhancement. By this method the shape of the enhanced image remains
the same as the original image. The objective function of the optimization
problem is formed to find a least squares solution of locality conditions. The
experimental results show that HBLPCE adapts well on images with various
statistical properties.

Huang
Lidong 9 proposed an image enhancement method CLAHE-DWT which combines both
CLAHE and DWT. The algorithm works in three stages. Initially the original image
is decomposed into low frequency and high frequency components by DWT. In the
second stage, low frequency coefficients are enhanced using CLAHE and high
frequency coefficients are unchanged to limit the noise. Finally the image is
reconstructed by taking the inverse DWT of the new coefficients. LEI, Noise
estimation, PSNR, MAE are the parameters used for evaluation. Results show
impressive performance and the over enhancement could be avoided

Anil Singh Parihar 10 proposes an entropy-based
dynamic sub-histogram equalization algorithm for contrast enhancement. A
recursive division of the histogram is performed based on the entropy of the
sub histograms. It provides a better distribution of intensity levels over the
entire dynamic range, which results in better contrast. It preserves the
original characteristics of the image resulting in contrast-enhanced images. Parameters
were not used. Results were compared with conventional contrast enhancement
algorithms.

Daeyeong
Kim 11 proposed an Adaptive Contrast Enhancement algorithm which is formulated

to
preserve the shape of the 1-D histogram and the statistical information on the
gray-level differences between
neighboring pixels obtained by a 2-D histogram. The proposed method
works in 2 stages. Initially, to
enhance the entire contrast by stretching the 1-D histogram while preserving
the shape of the histogram. Then to improve the details of nonsmooth areas
occurring frequently in input images. Constrained optimization problem
was formulated and the enhanced images were obtained using quadratic
programming. Experimental results show the enhanced images with good image
quality. Also the results show insufficient color quality and color root mean
enhancement measure (CRME)

Kristofor
B 12 proposed a new joint contrast
enhancement and turbulence mitigation (CETM) method that utilizes estimations
from the contrast enhancement algorithm to improve the turbulence removal
algorithm. An analysis of fog and turbulence is incorporated in this method.

Turbulence
Mitigation Metric (TMM) is also proposed to evaluate turbulence. It is observed
that removing fog before frame averaging is a better approach than removing fog
after frame averaging because of the depth discontinuities in scenes. For
removing turbulence it is common to average  the motion compensated images together in
order to remove the turbulent artifacts.

Mayank
Tiwari  13 proposed  a highspeed quantile-based histogram
equalisation (HSQHE) algorithm for contrast enhancement suitable for high
contrast digital images. HSQHE divides the
input image histogram into two or more sub-histograms, where
segmentation is based on quantile values and hence the entire spectrum of grey
level  plays a vital role in enhancement
process. The recursive segmentation of the histogram is not done, so only a
minimal time is required for segmentation.For the Assessment of contrast
enhancement PSNR, Entropy metrics are used. For Assessment of brightness
preservation AMBE is used. HSQHE preserves image brightness more accurately in
less time interval

Zhao
Wei 14  proposed a EMHM(Entropy
maximization histogram modification) method, which consists of dividing the
global histogram equalization into two steps, pixel populations mergence (PPM)
step and the grey-levels distribution (GLD) step .The histogram of input image
is merged with the proposed entropy maximisation  rule (EMR) in the PPM step, which can minimize
the reduction of entropy because of mergence and compression of the number of the number of grey scale
with non-zero pixel populations(GNPP) in output histogram. In the GLD step, the
new grey levels are redistributed using a LDF, which can alleviate the contrast
overstretching. Proposed method performs better than the existing methods.

Mohsen
Abdoli 15 proposed a new contrast enhancement method named GMMCE (Gaussian
mixture model-based contrast enhancement) to enhance low contrast images. This method models the histogram of low-contrast
image by the combination of a limited number of Gaussians where each Gaussian
presents a dominant intensity level of the image. This modelling attempts to
reflect the shape of a narrow histogram in the parameters of individual
Gaussians, to convey it to a broadened version. The global contrast enhancement
of the image was achieved by the enhancement of sub-histograms separated by the
mean value of the Gaussians of the GMM. Experimental results show that the
shape preserving method of GMMCE enhances the contrast of the image.

S.No

Authors

Title of the paper

Methodology

Features

1

Cheolkon Jung,
Tingting Sun

Optimized
Perceptual Tone Mapping for Contrast Enhancement of Images

Optimized
Perceptual Tone Mapping (OPTM)

Focuses on the
human visual attention by constructing a saliency histogram and performs
Contrast Enhancement

Improves the
performance without over enhancement

Needs more
time for CE compared to HE,CLAHE

2

Anil Singh
Parihar, Om Prakash Verma, Chintan Khanna

Fuzzy-Contextual
Contrast Enhancement

Fuzzy
dissimilarity histogram (FDHE), Fuzzy Contextual Contrast Enhancement (FCCE)

Captures the
intensity level differences in the neighborhood of the pixels

Global and
local CE.
No parameters are used.
Original shape
of histogram is preserved

EME measure is
low

3

Shilpa Suresh,
Shyam Lal, Chintala Sudhakar Reddy, Mustafa Servet Kiran

A Novel
Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images

Novel
enhancement algorithm (ACSEA)

Contrast
enhancement for satellite images

Improved
convergence rate.
Good
efficiency and robustness

Complex in its
execution

4

Huanjing Yue,
Jingyu Yang, Xiaoyan Sun, Feng Wu

Contrast
Enhancement Based on Intrinsic Image Decomposition

Split Bregman
algorithm and CLAHE

To enhance
images by estimating illumination and reflectance layers through intrinsic
image decomposition

Good
enhancement

Designed only
for CE. Cannot be used for methods like surface re-texturing, object
insertion etc

5

Se
EunKim,JongJu Jeon,IlKyuEom

Image contrast
enhancement using entropy scaling in wavelet domain

An entropy
based contrast enhancement method in the wavelet domain

Used in HSI
color space and performs image contrast enhancement

Color
information of low light images are good without any post processing.

Over-enhanced
regions exist

6

M.Shakeri,
M.H.Dezfoulian,
H.Khotanlou, A.H.Barati, Y.Masoumi

Image contrast
enhancement using fuzzy clustering with adaptive cluster parameter and
sub-histogram equalization

Contrast
enhancement algorithm based on local histogram equalization

Determination
of the number of sub-histograms and density based histogram division

Natural
appearance of images and enhanced the contrast

Loss of
details in high brightness levels of the image.
Noise in the
output image.

7

Lalit Maurya,
Prasant Kumar Mahapatra,
Amod Kumar

A social
spider optimized image fusion approach for contrast enhancement and
brightness preservation

A social
spider optimization (SSO)algorithm

Improvement in
sharpness, PSNR, brightness preservation

Better visual
quality

The number of
edge pixels of HE technique is high while the fitness value is less

8

Jeyong Shin,
Rae-Hong Park

Histogram-Based
Locality-Preserving Contrast Enhancement

Histogram-based
locality- preserving CE (HBLPCE)

To preserve
the localities of the histogram for performing contrast enhancement

images with various statistical properties.

Longer
Execution time of global CE for small images.

9

Huang Lidong,
Zhao Wei , Wang Jun, Sun Zebin

Combination of
contrast limited adaptive histogram equalisation and discrete wavelet
transform for image enhancement

Combines both
CLAHE and DWT

To enhance the
local details of an image

Performs well
in detail preservation and noise suppression.

High-frequency
component which contains most of
the noise in
original image is unchanged

10

Anil Singh Parihar , Om Prakash Verma

Contrast enhancement using entropy-based     dynamic sub-histogram equalization

Entropy-based
dynamic sub-histogram equalization algorithm (EDSHE)

Performs a
recursive division of the histogram based on the entropy of the sub histograms

Natural-looking,
good contrast images

Entropy
measure is less for few images.

11

Daeyeong Kim,
Changick Kim

Contrast
Enhancement Using Combined 1-D and 2-D Histogram-Based Techniques

Histogram

To preserve
the shape of the 1-D histogram the statistical information on the gray-level
differences

Enhanced  images and perceptual  image
quality

Processing
time is slower

12

Kristofor B.
Gibson and Truong Q. Nguyen

An Analysis
and Method for Contrast  Enhancement
Turbulence Mitigation

Contrast enhancement and turbulence mitigation (CETM)

Provides an analysis of

fog  turbulence

Increased
contrast
Less time
consumption

PSNR is very
low

13

Mayank Tiwari,
Bhupendra Gupta, Manish Shrivastava

High-speed
quantile-based histogram equalisation for brightness preservation and
contrast enhancement

Highspeed quantile-based histogram
equalisation (HSQHE)

Contrast enhancement
suitable for high contrast digital images

Image
brightness more accurately in less time interval

High PSNR
value only for certain images

14

Zhao Wei,
Huang Lidong, Wang Jun, Sun Zebin

Entropy
maximisation histogram modification scheme for image enhancement

Entropy
maximization histogram modification
(EMHM)

Divides the
global histogram equalization into two steps, pixel populations mergence
(PPM) step and the grey-levels distribution (GLD) step

Good
enhancement,
avoids
amplified noise and image artefacts.

Contrast
Overstretching problem

15

Mohsen Abdoli,
Hossein Sarikhani, Mohammad Ghanbari, Patrice Brault

Gaussian
mixture model-based contrast Enhancement

Gaussian
mixture model-based contrast enhancement)(GMMCE)

Uses Gaussian
mixture
modeling of
histograms
to model the
content of the images

Lowest
approximation error
highest
similarity to the original histogram
low-complexity
method

Subjective
Quality  measures not used

x

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