International Journals of Advanced Research in
Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
Research Article
June
2017
Analysis of Images through Avalanche Oozing Algorithm
Jatinder Kaur, Dr. Rajneesh Talwar
Department of Electronics and Communication Engineering, CGC TC, Jhanjeri,
Punjab, India
Abstract— Image percolating is appraised as one of the significant step in Image processing. In this paper a new
approach is presented for unblemished image. The proposed procedure is built on the multiplication of basic filtering
till we get undefiled output. Last step of the methodology of this work includes the pictorial comparison of predefined
filter results and propound technique outcomes.
Keywords— Oozing, Avalanche process, Prcolating
I. INTRODUCTION
The impetus of image analysis is to diminish the effects that degrade the visual quality of an image. A number of
strategies are used for osmoses the artifacts and yield a smooth and filtered image. These all algorithms comes under two
categories i.e., linear filtering and non linear filtering. The sufficient image intensity level is achieved through image
percolate. As a result it’s very convenient for the destination observer to the aggregate the complete information even
from the lesser number of pixels that are present at that time.
II. PAGE LAYOUT
The two methods used for cleaning of all unwanted effects from an image are linear filtering and non linear filtering.
The linear filtering methods are used in the places where edge details are not on the priority level. Direct smoothing
utilized for picture honing and picture coordinating ranges and so forth. Though non straight image filtering safeguard
the edge points of interest while doing clamor depleting [1 2].. The combine result of these two ways helps in vanishing
noise effect, enhances image contrast and provides edge details. The issues that adds on uncertain effects in image are
noises and artifacts. There are many types in noises i.e., salt and pepper, localvar, Gaussian noise and impulse noise etc.
The noise effects become a big obstacle during image detection and processing. So it’s become priority to dissipate
noises from at image during processing.
Direct and Indirect Filtering [3]
Linear unsharp, Average filter and wiener filter helps in Direct image filtering. Though for circuitous sifting of images
middle and versatile filtering utilized. Utilization of median and Averaging filtering are similar but in average noise
diminishing each yield pixel is set to a average of the pixel values in the area of the relating input pixel. However, with
median filtering, the value of an output pixel is determined by the midpoint of the neighborhood pixels, rather than the
mean. Median filtering relinquished better results by denigrate the sharpness of the image.
Unwanted Effects of Image
Noise is an unwanted effect that may degrade or corrupt the visual quality of an image. When image put under
processing stage, image may include cragged portions, In image processing, noise produces an image that may consist of
uneven lines, shadowy object, pixels deformation etc. These all types of effects are occur due to different types of noises
out of which some are Gaussian noise, speckle noise, impulse noise [4].
III. RELATED WORK
The two Monika Raghav et al [5] this paper depicts the image cleaning technique. As in medical science, image
processing becomes an apparent technology. So with its popularity, noise clearance mattering much to solve each
medical problem with efficiency. In this author described about two stages of image processing , image transmission and
image denoising. This paper also consist of some filtering methods description.
Rohit Verma, et al [6] , this paper includes the discussion about all possible techniques for noise degradation. Author
also explained about the reason of noise generation while capturing an image. According to authors, noise reduction
becomes major issue as image processing is used almost in every field, it may be education field, industry level and
medical field etc.
Priyanka kamboj et al [7] nowadays, image processing is an emerging technology. Image processing includes noise
diminishing methods to upgrade image quality and retain the originality of an image while processing. Noise model is
used for describing about various noises and their effects on image indices. This paper also aggregate direct and indirect
filtering techniques and pros. And cons. Of these methods.
Rahul Singh et al [8] , author discussed about various denoising techniques , their merits and demerits as well. Today’s
image processing cover-up all fields so it’s very important to preserve the quality of an image. Many researchers trying to
figure out the optimum methods for it.
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Page | 568
Kaur et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
Kanika Gupta et al [9] , in this paper, author focused on noise removal from test inputs. This paper also depicted some
brief description about usable strategies and filters for image percolating. Different techniques have been used for
filtering sample inputs.
IV. RELATED REDUCTION OF DEGRADATION FROM IMAGE BY AVALANCHE OOZING
Problem Formulation
Images qualities are usually depraved by unusual effects. Noises are one of them. While capturing an image or
transmit, noises perverted the originality of an image, which as a result affect the visual quality.
Conventionally, the outcome of noise reduction has stable impact on image processing techniques quality. Many
researchers provided variety of methods for image reduction but still these methods include some cons.
In this paper, proposed algorithm named as Avalanche Oozing yield outstanding results and uphold the image quality.
This strategy also helps to recoup the image quality.
Proposed filtering strategy and Results
Step1: First take two images as a input one by one
In first step, we have taken two sample inputs shown in figure1 and figure 2.
Step2: Image degradation by combining many degraded facts together
Step two furnished the degraded version of sample inputs. In this step different noises and artifacts have been prepend for
acquiring the corrupted sample inputs.
The subsequent figures (figure3 and figure4) depicted the deteriorate specimens image inputs.
Step3: Apply proposed Avalanche Oozing strategy on degraded sample inputs
In proposed tactics, after filtering through simple method, automatic repetition is started till we get clean image.
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Kaur et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
Figure 5 and figure 6 represented the trial inputs after the application of Avalanche Oozing algorithm. In the
subsequent paragraph comparison of proposed and predefined methods has been performed.
V. RESULTS AND DISCUSSION
Figures compiled in results and discussion paragraph till showed the results of sample corrupted images filtering
through predefined filters. But even after filtering still large degradation is present in the results. In Avalanche method,
through repetitive filtering the required results has been achieved shown in figure below (figure 11 and 12)
Images qualities are usually depraved by unusual effects. Noises are one of them. While capturing an image or transmit,
noises perverted the originality of an image, which as a result affect the visual quality.
Conventionally, the outcome of noise reduction has stable impact on image processing techniques quality. Many
researchers provided variety
VI. CONCLUSION
In this paper, we analyzed different percolating techniques for abolished noise degradation. Furthermore, we applied
proposed algorithm and compared outcomes of predefined and proposed Oozining . The outcome of proposed method
retains the image quality much better as compared to previous techniques.
Our future research will be focused on the using optimization algorithm for getting optimum value of image indices.
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Kaur et al., International Journals of Advanced Research in Computer Science and Software Engineering
ISSN: 2277-128X (Volume-7, Issue-6)
REFERENCES
[1]
K. Sethi and B. Chatterjee, "Machine recognition of constrained hand printed Devanagari", Pattern Recognition,
Vol. 9, pp. 69-75(1977).
[2]
G. L. Cash and M. Hatamian, "Optical Character Recognition by the method of moments", Computer Vision,
Graphics, and Image Processing, 39, 1987, pp. 291-310.
[3]
Kalpana, and Harjinder Singh, “ Review Paper:to study the image denoising techniques”, Vol. 02, pp.127-129,
Nov-2015.
[4]
Rohit Verma, and Dr. Jahid Ali,” A Comparative study of various types of image noise and efficient noise
removal techniques”, vol.3, issue 10, pp. 617-622, October 2013.
[5]
Monika Raghav, and Sahil Raheja,” Image Denoising Techniques: Literature Review”, vol. 3, pp. 5637-5641,
Issue 5, May-2014.
[6]
Rohit Verma, and Dr. Jahid Ali,” A Comparative study of various types of image noise and efficient noise
removal techniques”, vol.3, issue 10, pp. 617-622, October 2013.
[7]
Priyanka Kamboj et al.,” Brief study of various noise model and filtering techniques”, vol.4, No.4, pp.166-171,
April 2013.
[8]
Rahul singh et al,”brief review on image denoising techniques”,vol.04, pp.336-344, no. 01,April 2015.
[9]
Kanika Gupta et al.,” Image denoising techniques- a review paper”, vol. 02, issue 4, pp.6-9, March 2013.
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