Genetic Algorithm for High Dynamic Range Image
Using Contrast Enhancement
Deepali Agarwal
Dept. of Computer Science Engineering
VITM Gwalior
Gwalior (M.P), India
[email protected]
Abstract - Presents, for the purpose of acquiring ghost free high
dynamic range (HDR) pictures, we introduce another
methodology which is taking into account genetic algorithms. It is
the combination of numerous pictures which is in vision of HDR
system, just on condition is utilized for work which is no
development of an article and the camera when catching various,
diversely uncovered low dynamic range (LDR) pictures. Makes
three LDR pictures from a single input picture to take out such
an unlikely condition which is characterized in the proposed
calculation. Makes three LDR pictures from a single input
picture to rub out such a doubtful condition.Alsoa viable
algorithm to nearbyimprove the contrast of image is introduced.
This algorithm use histogram equalization and design a single
parameter to control the level of contrast enhancement with local
information of a picture, making the balance proportion fit with
Human Visual Perception.
Keywords—Genetic Algorithm; HDR; LDR; Edge preserving;
PSNR, MSE, Normalized Absolute Error, Contrast Enacement
force. Therefore, a solitary edge HDR calculation is required
for buyer portable imaging gadgets.[4].
However, color distortion and noise are increased amid the
recovery process.
Typical a only 256 values using by pixel for every of the blue,
red, and green channel is represented by cameras. On the
contradictory, the range of radiance of actual scene has a far
more extensive territory than the values of 256 [5]. Hence, a
photograph captured by a customary camera cannot capture
the complete dynamic scope of the scene radianceFor this for
the most part cameras pack scene radiance value applying a
precise capacity which is known as the camera response
function (CRF). This procedure can bring about districts of
upsetting under-or over-uncovered. Various routines have
been wanted to recuperate a dynamic range image (HDRI) by
assessing the camera reaction work by different low dynamic
range image (LDRIs) which are involved under a few
presentation settings for the scene [6].
I. INTRODUCTION
Acquisition of realistic photographs becomes easier for onexperts since high-quality imaging devices are popular in
customer hardware market. Three major variables for
reasonable securing incorporate; i) high spatial determination,
ii) HDR (high dynamic range), furthermore, iii) exact shading
generation. The HDR imaging system has recently emerged as
of late and played an important role in bringing a new
revolution to digital imaging [1]. While human vision can
perceive an extensive variety of brilliance levels more than
100,000, advanced imaging gadgets have a dynamic extent is
restricted in light of the limited number of bits of pixel
intensity values. To overcome restricted dynamic range, the
most conspicuous HDR imaging system gains numerous,
diversely uncovered info pictures and appropriately fuses them
to obtain the broadened shading array anddynamic range
[2][3].
Whereas the various picture based methods can effectively
give HDR pictures in the static scene, it creates ghost artifacts
if an item moves amid the presentation time. Also, it is much
more hard to gain movement free numerous edges utilizing a
minimized portable camera with constrained computational
CONTRAST ENHANCEMENT
The target of contrast enhancement is to obtain better visibility
of image details without introducing impractical visual
appearances and unnecessary artifacts. Contrast Enhancement
tunes the intensity f every pixels magnitude base on its
surrounding pixels. Contrast enhancement is classified into
indirect and direct method of contrast enhancement.
Histogram equalization and histogram specification are two
well-known indirect methods and contrast enhancement
known as direct method.[7]We use Histogram equalization
technique.
Histogram Equalization
Histogram basically is a graphic representation of the
distribution of data. The histogram shows how certain times a
specific gray level (intensity) appears in an image. Histogram
equalization is a technique for enhancing the contrast and
contrast adjustment in image processing. This technique
utilized in various applications areas such as example for
medical image processing, object tracking, speech recognition,
give the better views of bone structure in x-ray images, and to
enhanced detail in photographs backgrounds and foregrounds
which can be both bright or both dark[7]
HDR IMAGE RENDERING ALGORITHMS
HDR picture rendering calculations can be by and large
ordered by a handling system called a spatial preparing
strategies into two distinct classes: operators of local and
global [Reinhard et al. 2006]. The neighborhood
administrators is a specific mapping strategy utilized for each
pixel which is in view of its spatially limited substance, even
by for the applies global operator the similar transformation to
every pixel in the picture which is taking into account the
substance of global picture. Local operators take a wide range
of strategies for characterizing the spatial degree of the
operator, for example, multi-scale pyramids, edge preserving
low-pass filters or low-pass filters. It is Likewise, significant
to strain that for global operators it is not necessarily the
similar operator applied identically for each image, as global
operator can be a image knowledge function such as the
histogram. There are strengths and weaknesses approaches to
both the local and global tone-mapping. The spatial handling
of local operators has a tendency to be computationally extra
lavish, yet can allow for a more sensational reduction in the
total dynamic range.Theglobal operators have a tendency to be
computationally more straightforward and as a result can be
less difficult to actualize and quicker to execute.From the view
of rendering intents or aims, few algorithms goal to produce
the images that are visually appealing, using digital image
processing and photographic methods to the improve
rendering pleasantness, while another algorithms goal for
perceptual accuracy. These procedures try to mimic perceptual
qualities in range of luminance compressing addition, causing
in an image which provokes the similar visual reply as a
human may have when observing the similar actual scene.
Other procedures are planned to maximize total visible in the
images; a sample would be to utilization of high dynamic
range of medical pictures. Since the local algorithms are
effective of HDR compression furthermore have a tendency to
emulate the neighborhood versatile execution of the human
visual system,more emphasis was put on checking more of
these kinds of algorithms specifically.
II. RELATED WORKS
PankajA.Mohrut et. al[10], presents Image contrast
enhancement without influencing different parameters of an
image is one of the challenging tasks in image processing. The
nature of poor pictures can be enhanced utilizing different
picture image contrast enhancement technique. Complexity is
the visual contrast that makes an item discernable from
foundation. The fundamental point of this theme is to give an
enhanced and great quality picture by conforming the measure
of immersion and enlightenment to accomplish more
reasonable and clear image. This paper shows the relative
investigation of some famous image contrast enhancement
algorithms using histogram modification methods for example
histogram equalization, brightness preserving bi-histogram
equalization, dualistic sub-image histogram equalization,
recursive mean separate histogram equalization, dynamic
histogram equalization and enhancement using color and
depth image. However the conventional histogram
equalizations techniques have a portion of the downsides that
are overcame by utilizing joint segmentation method. image
contrast enhancement methods along with their advantages
and disadvantages and the improved versions of histogram
modification technique to enhance histogram based image
contrast using color and depth image.
AbhilashSrikantha et al. [11], offer a cutting-edge
investigation of the recently arranged methodology for
phantom free high element reach picture era. This paper offers
an order and consideration of the diverse systems is accounted
for to the serve as a helpful for future exploration direct on
this subject furthermore give the apparition issue in HDR
imaging is introduced and as of late proposed methods to deal
with this issue. All strategies correlation in this paper is in
perspective of a quantifiable assessment of the rightness of the
different methodologies in recognizing phantom districts in an
expected arrangement of exposures lastly the results introduce
that high differentiation development that is a moving item
different from the foundation and can be effectively identified
while little and low complexity developments. Little and low
difference developments are only the shading comparability
amidst the item and the foundation and are change hard to
recognize. This paper groups these methodologies in light of
the combination of the space, apparition map reckoning, the
setting of parameters, the quantity of exposures obliged and
the last produced HDR picture.
RamratanAhirwal, Yogesh Singh Rajput et al. [12], present an
ghost-free HDR imaging algorithm for picking up ghost-free
HDR imaging algorithm pictures. There is no development of
the camera and articles when catching various pictures and
distinctively uncovered LDR (low dynamic range) pictures
just in this condition the numerous picture combination based
HDR strategy lives up to expectations. This paper proposed
single picture based phantom free HDR imaging calculation
utilizing histogram detachment routines and edge preseving
de-noising system. Since the current numerous picture based
high element extent picture era method exertion just on
circumstance that there is no article development and camera
amid the securing of a few contrastingly uncovered LDR
pictures. Phantom relic is unavoidable in the dynamic
environment when getting numerous, distinctively uncovered
LDR pictures. To deal with Ghost ancient rarity issue, the
proposed calculation self produces three low element extent
pictures from a single inptr picture. For proposed calculation
framework utilizes strategies of histogram balance and for
evacuating noise intensification amid the process of histogram
evening out, uses the innovation that is edge saving
commotion concealment. At last produces HDR pictures by
melding three LDR pictures. The proposed technique that is
HDR creates phantom curio free HDR pictures by utilizing a
single input picture. Hence the proposed system gives simple
securing utilizing a camera without utilizing a tripod for
obtaining LDR pictures. Later on, it can be utilized as capacity
such as a part of cell telephone camera as incorporated
calculation or post-treatment to give the apparition curios free
HDR picture.
Harshitha B K1 et. al (2015)[15] The fundamental target of
upgrade is to prepare a picture so that the subsequent picture is
more suitable than the first picture for a particular application.
In this paper, a successful calculation to locally upgrade the
difference of image is introduced. This algorithm use
histogram equalization and design a single parameter to
control the level of difference upgrade with local information
of a picture, making the balance proportion fit with Human
Visual PerceptionIn this successful calculation for nearby
balance improvement the pictures with low differentiation are
fortified by referring so as to expand the difference proportion
to neighborhood data of pictures. Initially, adjusted histogram
evening out is connected to get an upgraded picture with solid
complexity. Second, a scientific model is utilized for
differentiation and morphological administrator is utilized to
modify the upgrade level of every pixel of an image locally.
Finally, an image is obtained with better contrast appropriate
for the human eye than global methods. The proposed
calculation appropriates 8-bit pictures, as well as can join a
tone propagation calculation to manage high element range
pictures. To adjust various types of picture store innovation, it
joins a successful tone-mapping calculation for High Dynamic
Range pictures to be pre-prepared, making it widely usable on
other kind of image formats..
GauravTiwari et.al, 2015 [13]In this paper we show that the
High-dynamic-range imaging (HDRI or HDR) is an
arrangement of systems utilized as a part of the imagingand
photography to recreate an unrivaled element scope of
iridescence than consistent advanced imaging or photographic
strategies can do. HDR pictures can indicate a predominant
scope of the luminance levels than can be accomplished
utilizing the additional "established" systems. Pictures, for
example those holding many actual-world scenes, from same
bright and direct sunlight to dangerous shade or very faint
nebulae. It is the frequently succeeded in getting and after that
joining various unique exposures of the comparative topic. A
Non-HDR cameras bring a photo with some constrained
measure of presentation extent, as at last bringing about the
harm of the point of interest in splendid or dull parts. In this
paper, we are learning about HDR picture, creating of HDR
picture furthermore learn about picture combination and
routines for picture fusioning. we comprehend about the Highdynamic-reach imaging (HDRI or HDR) is an arrangement of
techniques utilized as a part of the imaging and photography
to recreate an unrivaled element scope of glow than normal
advanced imaging or photographic systems can do.
Furthermore we are learning about HDR picture, producing of
HDR picture furthermore learn about picture combination and
strategies for picture fusioning.
Fakruddin et al. (2013) [14] The ghost problem in HDR (high
dynamic range) imaging is presented and also freshly
suggested approaches to solve this problem are reviewed. All
technique defines in detail and a association and classification
of the reviewed approaches are suggested. The comparison is
based on a quantitative evaluation of the correctness of the
various approaches in detecting ghost regions in a specified
sequence of exposures. We categorize the approaches based
on the fusion domain, the requirement for an apparition map
calculation, the quantity of exposures required, the setting of
parameters and the evacuation of phantom in the last HDR
picture. The results show that high contrast movement, i.e. a
moving object various from the background, can be properly
detected while low and small contrast movements, i.e.
similarity in colors between the object and the background,
which are very tough to find, are detected through multiple
thresholding, entropy, pixel order and prediction based
approaches. It can likewise be gathered that procedures, for
example graph-cuts based pixel arrange, different thresholding
and expectation based approaches well restrict the apparitions
while routines taking into account entropy have poor ghost
localization in the scene. Methods that joining exposures in
the domain of image are time-efficient as they avoid the
camera reply function tone mapping and estimation. On the
other different hand, approaches that consolidate exposures in
the brilliance area give a genuine high element range brilliance
map which may be helpful for later processing or show
applications Generally speaking, there is no single best
process and the selection of an method depends on the user’s
goal. For eliminating each moving objects in the last high
dynamic range image, iterative approaches achieve better
outcomes but are computationally luxurious. Approaches that
discard exposures affected through ghost in the combination,
thus by a subset of the input exposures sequence, offer decent
outcomes with low complexity. For keeping the moving object
at a fixed location in the combined high dynamic range image,
a decent ghost map is required at the ghost detection stage, To
eliminate ghosting artifacts this paper presented a process
capable of the catching as a higher dynamic range of a scene
as likely, without presenting ghosting. When the scene is
static, this method is equal to standard HDR methods;
otherwise it effectively defines which regions can be joint with
the reference image from another exposures in the stack so
that consistency is preserved. This procedure showed fruitful
on a variety of several scenarios, even when the motion
disturbs a substantial portion of the scene. In addition to
avoiding artifacts, it also permits the user to select the
reference frame so as to eliminate potentially annoying
objects. The knowledge can be long to other applications
requiring a stack of pictures, as we presented by images of de
noising are affected through ISO noise.
III. PROPOSED METHODOLOGY
Genetic AlgorithmIn this area we use Genetic algorithm [9] to determine the gray
level point that maximizes the Equation (3). So the Equation
(3) is the fitness function for GA and the aim is to expand the
fitness function. The process of the GA is given as.
Here T represents the number of generations(0, 1, 2…1000)
Step.1. In this step of genetic algorithm initial population of
size 64 is generated by randomly selecting the gray level point
from all the gray level point of an image.
Step.2. Fitness function that is f (τ)=H0 +H1 evaluated for
initial population. The gray level point that has
higherfitnessfunction worth is chosen as an ideal threshold
gray level point for that generation P.
Step.3. Perform the GA’s reproduction, and mutation operator
and make the next generation P+1.
Step.4. Get the last ideal threshold gray level point τ, if the
foreordained number of generations is come to or the optimal
threshold stays same for 10 generations, else return to Step
(2).
Toward the end of the GA, we get the gray level point τ for
which objective function has maximum value.
Using the specified gray level point τ, we split image
histogram dataset D into 2 subsets D0 and D1, which are
defined.
1.
Where x is original gray image
3. Edge-preserving de-noisingIt preserves the edges and remove noise. It filters the image
from noising.
Eavg(x,y)=[{1-β(x,y)}X r^ (x,y)]+[{β(x,y) X Eavg(x,y)}] (2)
Where Eavgis the output of averaging filter. The parameter β is a
weighting factor that is given by
β(m,n)=
1
(3)
1+𝛿(𝜎 2 )
Where δ represents a tuning parameter to make β distributed
as uniformly as possible in the scope of [0, 1] and σ2
represents the local variance.
4. LDR image fusionLDR image fusion is needed to combine the three LDR
images that are generated by the proposed algorithm and
create an HDR image. We employ only simple arithmetic
operator that fuses the similar coordinate pixel of three LDR
images and create the final HDR image that have same
coordinate pixel that have LDR images.
5.
Read Original Image
Figure2. Input Original Image
6.
Figure1. Initialize Genetic Algorithm Configuration
2. Histogram separation methodThis method that uses the idea of entropy based histogram
separation method and genetic algorithm, to specify stretching
region.
H = hist(x(:),[0:255])
(1)
Divide Image into three Images
(a)
(c)
(b)
(d)
Figure5. Image Dataset
Proposed Algorithm
The various steps followed for proving our proposed concepts
are :



Figure3. Show Normally Exposed, Under Exposed and Over
Exposed Image
7.
Output HDR Image







Figure4. Final HDR Image
8.Contrast Enacement using histrogramEqulization
Take ghosted Snap images captured by some sensing
device.
Now filter these images for further processing.
(From here onwards repeat steps for each image)
Apply HSV color transform to convert image into the
hsvcolor scheme.
Appy Genetic Algorithm to obtain different image
patterns by altering the individual components in the
previous result(i.e. under exposed, over exposed,
normal exposed).
Apply Edge preserving denoising on each image
obtained.
Transform the individual images to RGBcolor model.
Fuse three images into one to obtain the Sub HDR
image.
(After processing each image with above steps).
Fuse the three obtained HDR images into one.
After
get
HDR
final
image
use
HistrogramEqulaization
for
Image
Contrasr
Enhancement.
Table 1 MSE, PSNR, NCC and NAE value in the proposed
algorithm
Input
Image
(a)
(b)
(c)
(d)
PSNR
NCC
SC
NAE
21.7034
30.9875
24.7226
19.8379
0.7362
0.8627
0.8658
1.3720
1.8415
1.2733
1.3233
0.5291
0.2668
0.1493
0.1538
0.3801
Table 2 MSE, PSNR, NCC and NAE value in the base algorithm
In the experimental result of the proposed technique, mean
square error (MSE) and normalized absolute error (NAE) is
decreased as compared to the existing technique, likewise
peak signal to noise ratio (PSNR),Structural content (SC) and
normalized cross correlation (NCC) is better as compared to
the existing methods. For these reasons the nature of the HDR
image is better in the proposed algorithm.

Calculate Peak Signal Noise Ratio of HDR image.
1
𝑀𝑆𝐸(𝑥) = ∑𝑁
(𝑥 − 𝑦^)²
(4)
𝑁 𝑖=1
Where MSE(x) is mean square error between original image
and final HDR image, N is size of the original image, x is the
original image and y is the final HDR image.
𝑃𝑆𝑁𝑅 =
10𝑋𝑙𝑜𝑔(255𝑋255/𝑀𝑆𝐸)
(5)
𝑙𝑜𝑔10
Where PSNR is the peak signal noise ratio between original
image and final HDR image
𝑁𝐶𝐶 =
𝑠𝑢𝑚(𝑠𝑢𝑚(𝑥𝑋𝑦))
(6)
𝑠𝑢𝑚(𝑠𝑢𝑚(𝑥𝑋𝑥))
Where NCC is the normalized cross correlation of original
image and final HDR image. It is calculated for brightness
matching.
𝑁𝐴𝐸 =
𝑠𝑢𝑚(𝑠𝑢𝑚(𝑎𝑏𝑠(𝑥−𝑦)))
(7)
𝑠𝑢𝑚(𝑠𝑢𝑚(𝑥))
Where NAE is the normalized absolute error of original
image and final HDR image
𝑆𝐶 =
𝑠𝑢𝑚(𝑠𝑢𝑚(𝑥𝑋𝑥))
(8)
𝑠𝑢𝑚(𝑠𝑢𝑚(𝑦𝑋𝑦))
Where SC is the structural content of the original image and
final HDR image.
IV. RESULTS AND GRAPH ANALYSIS
Input
Image
(a)
(b)
(c)
(d)
PSNR
NCC
SC
NAE
23.5068
31.0315
26.5622
20.1608
0.7885
0.8626
0.9186
1.3698
1.6020
1.3231
1.2655
0.5353
0.2311
0.1399
1.3233
0.3707
Graph1:Image:Results on Best Fitness Value of Proposed
Method
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
Graph2 PSNR Comparison between Base vs Proposed System
CONCLUSION
We proposed single picture based HDR imaging calculation
utilizing histogram partition strategies and edge
preservingdenoising strategy with genetic algorithm.Ghost
free is important in the dynamic circumstance when securing
various, diversely uncovered LDR pictures. To deal with this
issue, the proposed calculation acquires three LDR pictures
from the single input picture. For this, we utilize methods of
histogram adjustment, genetic algorithm and edge preserving.
To eliminate noise with the assistance of edge preserving
noisesuppression technology. We acquire HDR picture
through intertwining three LDR pictures. The proposed
technique furnishes simple accomplishment with a camera
without by means of a tripod for securing LDR pictures. Later
on work,it can be utilized as capacity as a element of cell
telephone camera as incorporated calculation or post-treatment
to give the ghost artifacts-free HDR picture.It can conquer
camera’s limitation on the measure of color and luminance, it can
record is administered by the sensor's ability and the dynamic
scope of the camera's gadgets.
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