A Survey on Digital Image Enhancement Techniques

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 8, November 2010

A Survey on Digital Image Enhancement Techniques
V.Saradhadevi1 , Dr.V.Sundaram2
1
Research scholar, Karpagam University, 2 Director of MCA , karpagam Engineering College, Coimbatore, India.

Abstract---Image enhancement is one of the major research cause degradation of an image and image restoration is one of
fields in image processing. In many applications such as the key fields in today's Digital Image Processing due to its
medical application, military application, media etc., the wide area of applications. Commonly occurring degradations
image enhancement plays an important role. There are many include blurring, motion and noise. Blurring can be caused
techniques proposed by different authors in order to remove when object in the image is outside the camera’s depth of field
the noise from the image and produce the clear visual of the sometime during the exposure, whereas motion blur can be
image. Also, there are many filters and image smoothing caused when an object moves relative to the camera during an
methods available. All these available techniques are designed exposure. The general model for image degradation
for particular kind of noises. Recently, neural networks turn to phenomenon is given as y = Hf + n, where y is the observed
be a very effective tool to support the image enhancement. blurred and noisy image, f is the original image, n is additive
Neural network is applied in image enhancement because it random noise and H is the blurring operator. The main
provides many advantages over the other techniques. Also, objective is to estimate the original image from the observed
neural network can be suitable for removal of all kinds of degraded image. Whatever the degraded process, image
noises based on its training data. This paper provides survey distortions can fall into two categories, namely, spatially
about some of the techniques applied for image enhancement. invariant or space invariant and spatially variant or space
This survey deals with the several existing methods for image variant. In a space invariant distortion all pixels have suffered
enhancement using neural networks. the same form of distortion. This is generally caused by
problems with the imaging system such as distortions in
Keywords--- Image Enhancement, Image Denoising, Neural optical system, global lack of focus, or camera motion. In a
Network, Image Filter, Image Restoration. space variant distortion, the degradation suffered by a pixel in
the image depends upon its location in the image. This is
I. INTRODUCTION because of internal factors, such as distortions in the optical
system, or by external factors, such as object motion. This
The intention of image enhancement is to improve the survey provides many techniques available for image
interpretability or perception of data in images for human enhancement.
visual or to provide better input for other automated image
processing techniques. II. LITERATURE SURVEY

Image enhancement methods can be broadly divided into two Uma et al., [1] proposed a Morphological Neural Network for
categories: color image restoration. This paper considers the problem of
color image restoration degraded by a blur function and
• Spatial domain methods, which involves direct corrupted by random noise. A new approach developed by
operation on image pixels, and multilayer morphological (MLM) neural network is presented,
• Frequency domain methods, which involves Fourier which uses highly nonlinear morphological neuron for image
transform of an image for its operation. processing to get a high quality restored color image. In this
paper color images are considered into RGB distribution. Then
Regrettably, there is no general theory for determining what each subspace can be considered as a gray image space and is
good image enhancement is when it comes to human processed by morphological way used in gray images. This
perception. If it looks good, it is good! However, when image method is advantageous because of its low computational
enhancement methods are used as pre-processing tools for overhead, improved performance in terms of signal to noise
other image processing methods, then quantitative measures ratio with less number of neurons.
can decide which techniques are most suitable.
Gallo et al., [2] presented an adaptive image restoration using
Image Restoration is the technique of retaining the original local neural approach. This work aims at usage of neural
image from the degraded image given the knowledge of the learning for defining and experimentally evaluating an
degrading factors. There are a variety of reasons that could iterative strategy for blind image restoration in the presence of

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blur and noise. A salient aspect of the solution is the local image enhancement for gray scale images. But, the current
estimation of the restored image based on gradient descent study has adapted these techniques to be used for color
strategies able to estimate both the blurring function and the images. Even though the adapted image processing method is
regularized terms adaptively. As an alternative of explicitly quite simple, the results signify that these methods may have
defining the values of local regularization parameters through some potential to be used for improving the quality of Ziehl
predefined functions, an adaptive learning approach is Neelsen slide images. The experimental result illustrates that
proposed. both methods proposed by the author can improve the image
contrast and enhances the image quality when compared to its
The various restoration techniques used currently can be conventional techniques.
broadly viewed under two categories, namely, the transform
related techniques and the algebraic restoration techniques [3]. Pattern learning based image restoration using neural networks
The transform related techniques involve analyzing the is put forth by Dillon et al., [8]. The author illustrate a generic
degraded image after an appropriate transform has been pattern learning based image restoration scheme for degraded
applied. The two popular transform related techniques are digital images, where a feed-forward neural network is
inverse filtering and Kalman filtering [4]. Inverse filtering employed for implementation of the proposed techniques. The
produces a perfect restoration in the absence of noise, but the methodology reported here can be applied in several
presence of noise has very bad effects. The Kalman filter circumstances, for instance, quality enhancement as a post-
approach can be applied to non stationary image but it is processing of image compression schemes, blur image
computationally very intensive. restoration and noise image filter, provided that the training
data set is comprised of patterns rich enough for supervised
Algebraic techniques attempt to find a direct solution to the learning. This paper focuses on the problem of coded image
distortion by matrix inversion techniques, or techniques restoration. The key points addressed in this work are
involving an iterative method to minimize a degradation • The use of edge data extracted from source image as
measure. The two popular algebraic techniques available are a priori knowledge in the regularization function to
pseudo inverse filtering and constrained image restoration. get better details and reduce the ringing artifact of the
The pseudo inverse spatial image restoration techniques coded images.
attempt to restore an image by considering the vector space • The theoretic basis of the pattern learning-based
model of the image degradation and attempting to restore the technique using implicit function theorem.
image in this vector space domain. This method does not • Subjective quality improvement with the use of an
consider the effects of noise in the calculations of the pseudo image similarity for training neural networks
inverse and so is sensitive to noise in the image. This involves • Empirical studies with contrast to the set partitioning
determining an approximation to the inverse of the matrix in hierarchical tree (SPIHT) method.
blurring operator which is multiplied with the column scanned The main advantages of this model-based neural image
image vector to produce the degraded image. Blur matrices are restoration approach comprise strong robustness with respect
very large and it is not computationally feasible to invert them. to transmission noise and the parallel processing for real-time
Constrained restoration techniques are often based on Wiener applications.
estimation and regression techniques. One of the major
drawbacks in most of the image restoration algorithms is the Reeves [9] described fast and direct image restoration with
computational complexity, so various simplifying assumptions edge-preserving regularization. In several applications, fast
have been made to obtain computationally feasible algorithms. restorations are required to keep up with the frame rate. FFT-
based restoration affords a fast implementation, but it does so
Motivated by the biological neural network in which the at the expense of assuming that the degree of regularization is
processing power lies in a large number of neurons linked constant over the image. Unfortunately, this hypothesis can
with synaptic weights, artificial neural network models generate significant ringing artifacts in the presence of edges
attempt to achieve a good performance via dense as well as edges that are blurrier than necessary. Shift-variant
interconnection of simple computational elements. Neural regularization affords a way to vary the roughness penalty as a
network models have great potential in areas where high function of spatial coordinates. Virtually all edge-preserving
computation rates are required and the current best systems are regularization techniques exploit this concept. However, this
far from equaling human performance. Restoration of a high technique destroys the structure that makes the use of the FFT
quality image from a degraded recording is a good application possible, since the deblurring operation is no longer shift-
area of neural nets. Joon et al., [5] proposed a Modified invariant. Thus, the restoration techniques available for this
Hopfield neural network model for solving the restoration problem no longer have the computational efficiency of the
problem which improves upon the algorithm proposed by FFT. The author proposes a new restoration method for the
Zhou et al. [6]. shift-variant regularization approach that can be implemented
in a fast and flexible manner. This paper decomposes the
Osman et al., [7] gives an image enhancement using bright restoration into a sum of two independent restorations. One
and dark stretching techniques for tissue based tuberculosis restoration yields an image that comes directly from an FFT-
bacilli detection. This paper proposes two methods for color based approach. This image is a shift-invariant restoration
image enhancement; bright stretching and dark stretching consisting of usual artifacts. The other restoration involves a
algorithms. Both techniques are well known to create good

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set of unknowns whose number equals the number of pixels proposed in this paper. The approached operator utilizes the
with a local smoothing penalty significantly different from the gradient operator to create the image enhancement processing
typical value in the image. This restoration represents the focus on the interested regions, and the OTSU operator to
artifact correction image. By summing the two, the artifacts automatically select the best threshold value, which can realize
are canceled. Since the second restoration has a significantly a novel adaptive fuzzy image enhancement algorithm for local
reduced set of unknowns, it can be calculated very efficiently regions. Through the experimentations of the asphalt
even though no circular convolution structure exists. pavement crack image detection system, the experimental
results specify that the novel algorithm can not only attain
Noise-refined image enhancement using multi-objective better processing effects and higher processing speed than
optimization is illustrated by Peng et al., [10]. This paper now-available fuzzy image enhancement algorithms, but also
presents a novel scheme for the enhancement of images using possess the property of high practicability and generality.
stochastic resonance (SR) noise. In this scheme, a suitable
dose of noise is added to the lower quality images such that Faouzi et al., [13] provides a directional-rational approach for
the performance of a suboptimal image enhancer is improved color image enhancement. In this, the author presents an
without altering its parameters. In this paper, image unsharp masking-based approach for noise smoothing and
enhancement is modeled as a constrained multi-objective edge enhancing in multichannel images. The structure
optimization (MOO) problem, with similarity and some presented by author is similar to the conventional unsharp
desired image enhancement characteristic being the two masking structure, however, the enhancement is allowed only
objective functions. The principle of SR noise-refined image in the direction of maximal change and the enhancement
enhancement is analyzed, and an image enhancement system parameter is computed as a nonlinear function of the rate of
is developed. A genetic algorithm-based MOO technique is change. This scheme improves the true details, limits the
employed to find the optimum parameters of the SR noise overshoot near sharp edges and attenuates noise in flat areas.
distribution. In addition, a novel image quality evaluation In addition the use of the control function eliminates the need
metric based on human visual system (HVS) is developed as for the subjective coefficient λ used in the conventional
one of the objective functions to guide the MOO search unsharp masking method.
procedure.
The noise reduction based on fuzzy image filtering is put forth
Lu et al., [11] proposed an image noise reduction technique by Dimitri et al., [14]. A new fuzzy filter is provided for the
based on the fuzzy rules. Considering the image as non- noise reduction of images corrupted with additive noise. The
stationary signal, an image noise reduction method based on filter involves two stages. The initial stage calculates a fuzzy
the fuzzy rules is proposed. This image processing system derivative for eight different directions. The next stage uses
(IPS) is recognized as a time-variant system in which the these fuzzy derivatives to carry out fuzzy smoothing by
system parameters change continuously based on the local weighting the contributions of neighboring pixel values. Both
characteristics of the images. For the purpose of noise these stages are dependent on fuzzy rules which make use of
reduction, Gaussian noise is considered here. The fuzzy rules membership functions. The filter can be implemented
are implemented to consider the unstableness and uncertainty iteratively to effectively decrease heavy noise. Especially, the
of signals. The nonlinear function indicating the fuzzy rule- shape of the membership functions is adapted according to the
based IPS depends on the rules concerning the local remaining noise level after each iteration; making use of the
characteristics of the input, on the membership functions, and distribution of the homogeneity in the image. A statistical
on the used defuzzification method. For making the system technique for the noise distribution can be included to relate
performance as high as possible, these factors must be agreed the homogeneity to the adaptation scheme of the membership
to be the most appropriate ones. In this paper a technique for functions.
designing the optimum nonlinear function directly from the
local characteristics of training data is presented. Here the Gacsadi et al., [15] makes use of cellular neural network for
rules, the membership functions, and the technique of the purpose of image enhancement. This technique takes both
defuzzification are not essential to be known. The design of the denoising and the increase of the contrast into
these factors is concerned in the design of the membership consideration. Due to whole parallel processing, computing-
function, thus attaining the optimum nonlinear function is time reduction is achieved. In the enhancement process by
sufficient for designing the IPS. The only thing required to do usage of nonlinear and feedback template local and also
is to choose what sort of the local characteristics of the image regional properties will be taken into consideration due to the
should be applied to the rule-based system. Computer propagation of the effect between the neighbors. Considerable
simulations illustrate that the proposed technique gives better computing power is required to solve the image processing
results in comparison with that of the weighted averaging filter task described by variational computing. The Cellular Neural
and median filter. Networks (CNN) proved to be very useful regarding real-time
image processing. The reduction of computing time, due to
An adaptive fuzzy image enhancement algorithm for local parallel processing, can be obtained only if the processing
regions is given by Yan et al., [12]. To overcome the algorithm can be implemented on a CNNUC or by using
drawbacks of low speed and losing image information in fuzzy emulated digital CNN-UM implemented on FPGAs.
image enhancement algorithms, a novel fuzzy enhancement
operator with close-character and transplantable-character is

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The traditional analog architectures of CNN-UC are superior appropriate for enhancement because the color components
in terms of processing speed and power dissipation. However, are not decoupled. Alternatively, in HSV color model, hue
these implementations have a restricted applicability due to (H), the color content, is separate from saturation (S), which
their limited features regarding accuracy, flexibility, small can be used to dilute the color content and V, the intensity of
number of cells, their high cost and the lengthy period needed the color content. By conserving H, and modifying only S and
for the development of such a chip. On the other hand, while V, it is likely to enhance color image. Therefore, it is required
software solutions are extremely flexible, they are sometimes to convert RGB into HSV for the purpose. A Gaussian type
inefficient because of limited and low processing speed. membership function is utilized to model S and V property of
Today, the version of CNN digital emulator implemented the image. This membership function utilizes only one
FPGA is a solution that achieved a compromise between speed fuzzifier and is evaluated by maximizing fuzzy contrast.
and accuracy, but ensure repeatability, reproducibility,
flexibility, possibility for CNN implementation even for Muthu Selvi et al., [18] put forth a hybrid image enhancement
complex processes of processing and easy interfacing with technique for noisy dim images using curvelet and
digital systems. In this sense, the CNN digital emulator morphology techniques. The noisy dim images degrade the
implemented FPGA maximizes, for a concrete application, the image quality. The denoising method using curvelet transform
performances of the overall CNN processing. outperforms than wavelet transform. The noisy dim image is
made noise free with the help of curvelet transform to the dim
There are two basic approaches to image denoising [16] - image for avoiding the over illumination and under
spatial filtering methods and transform domain filtering illumination problems. Next the dim image is enhanced using
methods. the morphological transformations. Closing by reconstruction
is implemented to identify the background of the dim image.
Spatial Filtering The experimental result shows that the morphological
restoration filter with closing by reconstruction produces
A conventional way to remove noise from image data is to better result than opening by reconstruction.
employ spatial filters. Spatial filters can be further classified
into non-linear and linear filters. Hassan et al., [19] presented a contrast enhancement technique
for dark blurred images. The chief goal presented by the
i. Non-Linear Filters author is to produce a contrast enhancement technique to
With non-linear filters, the noise is removed without any recover an image within a given area, from a blurred and
attempts to explicitly identify it. Spatial filters utilize a low darkness specimen, also improve visual quality of it. This
pass filtering on groups of pixels with the assumption that the method consists of two steps unsharp masking step and
noise occupies the higher region of frequency spectrum. contrast enhancement step. The unsharp masking step is
Generally spatial filters remove noise to a reasonable extent applied to the image to sharpen edges and bring out hidden
but at the cost of blurring images which in turn makes the details. On the contrary enhancement step 3x3 slider map
edges in pictures invisible. In recent years, a variety of window was applied to the image to determine if the
nonlinear median type filters such as weighted median, rank corresponding pixel will be remapped or not. The new value
conditioned rank selection, and relaxed median have been of remapped pixel obtained is based on a sigmoid map
developed to overcome this drawback. function. Good and satisfying results were obtained by
experimentation on this technique.
ii. Linear Filters
A mean filter is the optimal linear filter for Gaussian noise in Gilbao et al., [20] uses the complex diffusion processes for
the sense of mean square error. Linear filters also tend to blur image enhancement and denoising. The linear and nonlinear
sharp edges, destroy lines and other fine image details, and scale spaces, obtained by the inherently real-valued diffusion
perform poorly in the presence of signal-dependent noise. The equation, are generalized to complex diffusion processes, by
wiener filtering method requires the information about the incorporating the free Schrodinger equation. A basic solution
spectra of the noise and the original signal and it works well for the linear case of the complex diffusion equation is
only if the underlying signal is smooth. Wiener method developed. Investigation of its performance shows that the
implements spatial smoothing and its model complexity generalized diffusion process combines properties of both
control correspond to choosing the window size. forward and inverse diffusion. It verifies that the imaginary
part is a smoothed second derivative, scaled by time, when the
Sarode et al., [17] proposed the color image enhancement with complex diffusion coefficient approaches the real axis. Based
the help of fuzzy system. This technique involves the use of on this observation, the authors develop two examples of
knowledge-base (fuzzy expert) systems that are capable of nonlinear complex processes, useful in image processing: a
mimicking the behavior of a human expert. Fuzzy technique regularized shock filter for image enhancement and a ramp
of knowing severity of tumor is essential to determine if there preserving denoising process.
is a need for the biopsy and it gives to user a clear idea of
spread and severity level of tumor. Fuzzy based improvement Image denoising using non-negative sparse coding shrinkage
of color feature of tumor is an application of fuzzy in the area algorithm is given by Shang et al., [21]. The author proposes a
of color feature extraction for enhancement of a peculiar new method for denoising natural images using this extended
feature. It has been determined that RGB color model is not non-negative sparse coding (NNSC) neural network shrinkage

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algorithm, which is self-adaptive to the statistic property of proposed method offering the best efficiency in terms of
natural images. The fundamental principle of denoising using image denoising and edge preservation.
NNSC shrinkage is similar to that using standard sparse
shrinkage and wavelet soft threshold. Using test images Zhang et al., [25] presented image denoising using a neural
corrupted by additive Gaussian noise, this paper evaluated the network based non-linear filter in wavelet domain. Images are
method across a range of noise levels. This method utilized the often distorted as a result of various factors that can occur
normalized mean squared error as a measure of the quality of during acquisition and transmission processes. Image
denoising images and the signal to noise rate (SNR) value as denoising is intended at removing or reducing noise, so that a
an evaluative feature of different denoising approaches. The good-quality image can be obtained for various applications.
experimental result shows that the NNSC shrinkage certainly The author presents a neural network based denoising method
is effective in image denoising. Otherwise, the author also implemented in the wavelet transform domain. A noisy image
compares the effectiveness of the NNSC shrinkage with sparse is first wavelet transformed into four subbands, and then a
coding shrinkage and wavelet soft threshold method. The trained layered neural network is applied to each subband to
simulative tests show that this denoising method outperforms generate noise-removed wavelet coefficients from their noisy
any other of the two kinds of denoising approaches. ones. The denoised image is then obtained through the inverse
transform on the noise-removed wavelet coefficients.
Gupta et al., [22] designed a FIR filter for image restoration Compared with other techniques performed in the wavelet
using principal component neural network. The neural domain, it requires no a priori knowledge about the noise and
network can be applied in many image denoising applications needs only one level of signal decomposition to obtain very
because of its inherent characteristics such as nonlinear good denoising results.
mapping and self-adaptiveness. The design of filters widely
depends on the a-priori knowledge about the type of noise. III. CONCLUSION
Because of this, standard filters are application and image
specific. Extensively used filtering algorithms reduce noisy This survey discusses about several existing image
artifacts by smoothing. Though, this operation normally enhancement methods. All those methods discussed have their
results in smoothing of the edges as well. Alternatively, own advantages and disadvantages. This survey helps in
sharpening filters enhance the high frequency details making choosing the better suitable image enhancement scheme for
the image non-smooth. An integrated general technique to particular kind of noise in the image. Also, the filtering
design a finite impulse response filter based on principal algorithms used for removing the noise from the image are
component neural network (PCNN) is provided in this study presented in this thesis. This survey explains about the
for image filtering, optimized in the sense of visual inspection importance of neural network for image enhancement as the
and error metric. This technique utilizes the inter-pixel neural networks have the advantages such as nonlinear
correlation by iteratively updating the filter coefficients using mapping and self-adaptiveness. To overcome the demerit of
PCNN. This technique performs optimal smoothing of the the techniques discussed in this survey, Adaptive Neuro-Fuzzy
noisy image by preserving high and low frequency features. Interference Systems (ANFIS) can be used for image
Experimental results state that this filter is robust under enhancement as it combines the advantages of Artificial
various noise distributions. Additionally, the number of Neural Networks (ANN) and Fuzzy Interference System
unknown parameters is very few and most of these parameters (FIS).
are adaptively obtained from the processed image.

Image denoising based on combined neural networks filter is REFERENCES
proposed Junhong et al.,[23]. A new image restoration
technique based on combined neural networks Alter is [1] S. Uma and S. Annadurai, "Color Image Restoration Using
proposed by the author. This integrated neural networks Alter Morphological Neural Network", ICGST.
is posed by a BPNN Alter and an image data fusion system [2] I. Gallo, E. Binaghi and A. Macchi, "Adaptive Image Restoration using
based on self-organizing mapping neural networks. And this a Local Neural Approach".
[3] H. C. Andrews & B. R. Hunt, "Digital Image restoration", Englewood
technique can use the corrupted image itself as training data to cliffs, NJ, Prentice Hall, 1977.
avoid the problem of how to choose the training data, which is [4] Rafael C. Gonzalez and Richard E. Woods, "Digital image processing",
most of the other neural networks denoising methods have to 2nd edition, Addison- Wesely, 2004.
face, by using the distributed character of WGN. Experiment [5] Joon K. Paik and Aggelos K. Katsaggelos, "Image restoration using a
modified Hopfield Network", IEEE Transactions on image processing,
results show that this method can denoise the noises Vol 1, No.1, pp. 49-63, January 1992.
effectively. [6] Y.T.Zhou, R. Chellappa and B.K. Jenkins, "Image restoration using a
neural network", IEEE Trans. Acoust., Speech, Signal Processing, Vol,
Gacsadi et al., [24] describes PDE-based medical images ASSP-36, pp 1141-1151, July 1988.
[7] M.K. Osman, M.Y. Mashor and H. Jaafar2Colour, "Image Enhancement
denoising using Cellular Neural Networks. The author using Bright and Dark Stretching Techniques for Tissue based
presents the medical image denoising by using cellular neural Tuberculosis Bacilli Detection", Proceedings of the International
networks (CNN), based on the variational model of Chan and Conference on Man-Machine Systems (ICoMMS), 2009.
Esedoglu. By comparatively examining the proposed method [8] Dianhui Wang Dillon and T. Chang, "Pattern learning based image
restoration using neural networks", Proceedings of the International
and other CNN methods that uses variational computation, the Joint Conference on Neural Network, 2002.

177 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 8, November 2010

[9] S.J. Reeves, "Fast and direct image restoration with edge-preserving
regularization", IEEE Digital Signal Processing Workshop, 2002.
[10] Renbin Peng, Hao Chen and Varshney, "Noise-refined image
enhancement using multi-objective optimization", 44th Annual
Conference on Information Sciences and Systems (CISS), 2010.
[11] Ruihua Lu and Li Deng, "An Image Noise Reduction Technique Based
on the Fuzzy Rules".
[12] Yan Maode, Bo Shaobo, Li Xue and He Yuyao, "An Adaptive Fuzzy
Image Enhancement Algorithm for Local Regions", Chinese Control
Conference, 2007.
[13] Faouzi Alaya Cheikh and Moncef Gabbouj, "Directional-Rational
Approach for Color Image Enhancement".
[14] Dimitri Van De Ville, Mike Nachtegael, Dietrich Van der Weken,
Etienne E. Kerre, Wilfried Philips, and Ignace Lemahieu, "Noise
Reduction by Fuzzy Image Filtering", IEEE transactions on fuzzy
systems, vol. 11, no. 4, august 2003.
[15] A. Gacsadi, V. Tiponut, E. Gergely, I. Gavrilut, "Variational Based
Image Enhancement Method by using Cellular Neural Networks",
Proceedings of the 13th WSEAS International Conference on
SYSTEMS.
[16] Mukesh C. Motwani, Mukesh C. Gadiya, Rakhi C. Motwani and
Frederick C. Harris, Jr., "Survey of Image Denoising Techniques".
[17] Milindkumar V. Sarode, S.A.Ladhake and Prashant R. Deshmukh,
"Fuzzy system for color image enhancement", World Academy of
Science, Engineering and Technology, 2008.
[18] Muthu Selvi, Roselin and Kavitha, "A Hybrid Image Enhancement
Technique for Noisy Dim Images Using Curvelet and Morphology",
International Journal of Engineering Science and Technology Vol. 2(7),
2010.
[19] Naglaa Yehya Hassan and Norio Aakamatsu, "Contrast Enhancement
Technique of Dark Blurred Image", IJCSNS International Journal of
Computer Science and Network Security, Vol.6, No.2A, February 2006.
[20] Gilboa.G, Sochen.N and Zeevi.Y.Y, "Image enhancement and denoising
by complex diffusion processes", IEEE Transactions on Pattern Analysis
and Machine Intelligence, 2004.
[21] Li Shang and Deshuang Huang, "Image denoising using non-negative
sparse coding shrinkage algorithm", IEEE Computer Society
Conference on Computer Vision and Pattern Recognition, 2005.
[22] Gupta.P.K and Kanhirodan.R, "Design of a FIR Filter for Image
Restoration using Principal Component Neural Networks", IEEE
International Conference on Industrial Technology, 2006.
[23] Junhong Chen and Qinyu Zhang, "Image Denoising Based on Combined
Neural Networks Filter", International Conference on Information
Engineering and Computer Science, 2009.
[24] Gacsadi.A, Grava.C, Straciuc.O and Gavrilut.I, "PDE-based medical
images denoising using Cellular Neural Networks", International
Symposium on Signals, Circuits and Systems, 2009.
[25] Zhang.S and Salari.E, "Image denoising using a neural network based
non-linear filter in wavelet domain", IEEE International Conference on
Acoustics, Speech and Signal Processing, 2005.

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