Project presentation on BACKGROUND DETECTION AND ENHANCEMENT OF IMAGES WITH POOR LIGHTING USING MORPHOLOGICAL TRANSFORMATION Department of Electronics and communication Engineering SREE VIDYANIKETHAN ENGINEERING COLLEGE Sri Sainathnagar,A.Rangampet,Tirupathi-517102 Under the guidance of Ms. H.D. PRAVEENA, M.Tech., ASSISTANT PROFESSOR Department of ECE, SVEC By M.Papaiah M.Tech(DECS) RollNo:10121D3809
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Project presentation on
BACKGROUND DETECTION AND ENHANCEMENT OF IMAGES WITH POOR LIGHTING
USING MORPHOLOGICAL TRANSFORMATION
Department of Electronics and communication Engineering
SREE VIDYANIKETHAN ENGINEERING COLLEGE
Sri Sainathnagar,A.Rangampet,Tirupathi-517102
Under the guidance of
Ms. H.D. PRAVEENA, M.Tech., ASSISTANT PROFESSOR Department of ECE, SVEC
ByM.Papaiah
M.Tech(DECS)RollNo:10121D3809
OUTLINE
Objective
Introduction
Existing Methods
Proposed Method
Block Diagram of proposed Method
Morphological operations
Morphological Operations on image
Detection of image background by Using Block Analysis
Simulation Results
Image background Using opening by reconstruction
References
The main objective of this project is to detect the background
image and enhance the contrast in gray level image with poor
lighting using some morphological transformations.
First operator applies information from block analysis and
second operator’s uses opening by reconstruction.
OBJECTIVE
The contrast enhancement problem in digital images occurs in many
fields like medical field, satellite imaging field, communication field
and radar imaging field.
In this poor lighting images it is difficult to know the actual information
like back ground. This work deals with the detection of background in
images with poor contrast.
Morphological transformations are used to detect the background in
images characterized by poor lighting.
Contrast image enhancement has been carried out by the application
of two operators based on the Weber’s law notion.
INTRODUCTION
EXISTING METHODS
There are number of existing systems based on the mathematical
morphological methodologies.
These existing methods are based on statistical analysis, such as the
Global & Local Histogram equalization.
The main disadvantage of histogram equalization is that the global
properties of the image can not be properly applied in local context
and producing poor performance in detail preservation.
PROPOSED METHOD
Contrast image enhancement has been carried out by the
application of two operators based on the Weber’s law notion.
The first operator employs information from block analysis, while
the second transformation utilizes the opening by reconstruction,
which is employed to define the multi-background notion.
The contrast operators consists in normalizing the grey level of the
input image with the purpose of avoiding abrupt changes in intensity
among the different regions.
the performance of the proposed operators is illustrated through the
processing of images with different backgrounds, the majority of them
with poor lighting conditions.
MATLAB simulation has to be carried out for the Proposed method
Block Diagram of Proposed Technique
Erosion
Every object pixel that is touching a background pixel is changed into a background pixel.
Erosion makes the objects smaller
0 1 0
1 1 1
0 1 0
Structuring ElementOriginal Image Effect of Erosion
Morphological operations
Dilation
Every background pixel that is touching an object pixel is changed into an object pixel., and can break a single object into multiple objects.
Dilation makes the objects larger, and can merge multiple objects into one.
Effect of dilation using a 3×3 square structuring element
Opening
The opening is an erosion followed by dilation. Opening removes small islands and thin filaments of object pixels.
The opening of the dark-blue square by a disk, resulting in the light-blue
square with round corners. The opening of A by B is obtained by the
erosion of A by B, followed by dilation of the resulting image by B:
Closing
The closing operation is dilation followed by erosion. Closing removes
islands and thin filaments of background pixels.
This technique is useful for handling noisy images where some
pixels have wrong binary value.
MORPHOLOGICAL OPERATIONS ON IMAGE
(a) Original image f and marker g = Ɛ (f)
(b) original image f and marker g = δ (f)
(c) Opening by reconstruction using erosion as maker
(d) closing by reconstruction using dilation as marker
Detection of image background by using block analysis
Figure: Background condition obtained by block analysis
The image ‘f’ is divided into ‘n’ blocks
Each block is a sub image of the original image. The minimum and maximum intensity values in each sub image are denoted as
For each analyzed block, maximum and minimum values are used to determine the background criteria using
The value of represents a division line between clear f ˃ ) and dark (f < ) intensity levels. Once ( ) is calculated, this value is used to select the background parameter associated with the analyzed block.
Expression to enhance the contrast is proposed:
The background parameters depends on the value. If (f< ) (dark region). The background parameter takes the value of maximum intensity and minimum intensity for clear region.
and Maximum and minimum intensity values and expression for
and Values correspond to the morphological dilation and erosion.
Then expression for
Was substituted by
Expression for contrast operator is
SIMULATION RESULTS
Fig: Erosion operation
• qqqq
Fig: Dilation operation
------ Dilation image
Fig: Opening operation
Fig: Closing operation
(a) Original image
(b) Block analysis
(c) Output image
Image Background Using Opening by Reconstruction
In opening by reconstruction image background is detected without
dividing the original image into block and without using the
morphological erosion and dilation.
Morphological transformations generate new contours when the
structuring element is increased.
Opening by reconstruction allows the modification of the altitude of
regional maxima when the size of the structuring element increases.
The effect can be used to detect background criteria
Expression to enhance the contrast in image with poor lighting in opening by reconstruction
First, a methodology was introduced to compute an approximation to the
background using blocks analysis. This proposal was subsequently extended
using mathematical morphology operators. However, a difficulty was detected
when the morphological erosion and dilation were employed; therefore, a new
proposal to detect the image background was propounded, that is based on
the use of morphological connected transformations.
Also, morphological contrast enhancement transformations were introduced.
Such operators are based on Weber’s law notion.
CONCLUSIONS
REFERENCES[1].Z.Liu C.Zhang and Z.Zhang, “Learning –based perceptual image quality
improvement for video conferencing,” presented at the IEEE Int. conf. Multimedia and Expo (ICME), Beijing, China, Jul.2007.
[2].R.H.Sherrier and G.A.Johnson, “Regionally adaptive histogram equalization of the chest,” IEEE Trans.Med.Imag.,vol.MI-6, pp.1-7,1987.
[3].A.Majumder and S.Irani, “perception-based contrast enhancement of images,” ACM Trans.Appl.percpt.,vol.4, no.3, 2007, Article17.
[4].A.K.Jain Fundamentals of Digital Images Processing. Englewood cliffs, NJ:Prentice-Hall,1989.
[5].C.R.Gonzalez and E.Woods, Digitl Image Processing Englewood Cliffs, NJ:Prentice Hall,1992.