A Hybrid Image Enhancement Technique for
Noisy Dim Images Using Curvelet and
Morphology
Muthu Selvi1 Roselin2 and Dr.Kavitha3
1
Department of Computer Science and Engineering, Anna University, Tirunelveli, muthu_lak2007@yahoo.co.in
2
Department of Computer Science and Engineering, Anna University, Tirunelveli, roselin_js@rediffmail.com
3
Department of Computer Science and Engineering, Anna University, Tirunelveli
ABSTRACT
The noisy dim images degrade the image quality. The new denoising method using curvelet transform outperforms than wavelet transform. The noisy dim image is denoised with the help of curvelet transform to the dim image for avoiding the over illumination and under illumination problems. Next the dim image is enhanced using the morphological transformations. Closing by reconstruction is used to detect the background of the dim image. Finally the results are tested from noisy dim images and also compared the results with opening by reconstruction. Morphological reconstruction filter closing by reconstruction produces better result than opening by reconstruction.
KEYWORDS
Denoising, Curvelet Transform, Gamma Correction, Opening By Reconstruction, Closing By Reconstruction,
1.INTRODUCTION
The image quality is an important factor for the understandability of the human vision. The image usually has noise which is not easily eliminated in image processing. The quality of the image is affected by the presence of noise and the images taken from dim image. These two problems are treated in different way. The image usually has noise which is not easily eliminated in image processing. There are many denoising methods are used. The recent method curvelet transform for denoising in [2] outperforms wavelet transform.
Morphological openings and closings are useful for smoothing of grayscale images. But it also takes the thin features along with noise. MIC (Morphological Image Cleaning) algorithm which is explained in [1] preserves thin features while removing noise. In [3] the image denoising using a statistical model for local patches of wavelet coefficients using an infinite mixture of Gaussians ,in which the mixture depends on two hidden variables representing local amplitude and local orientation. The orientation variable leads to improvements in PSNR. The fingerprint image denoising using the curvelets transform [4] has produced the results better than wavelet transform. The hard threshold is applied to the coefficients after decomposition. Four types of noise, viz. Random noise, Gaussian noise, Salt and Pepper noise and Speckle noise, were chosen for mixing with fingerprint image. For each type of noise, the extent of mixing corresponded to the standard deviations of 0.1, 0.15,0.2,0.25,0.3,0.35. In all cases it was found that in terms of PSNR the curvelet transform dominates the wavelet transform.
2.IMAGE ENHANCEMENT FOR NOISY DIM IMAGE
Image Enhancement is a process that refers to segmentation, denoising, contrast enhancement, Morphological Reconstruction etc. The noisy dim image is the input image. Then apply curvelet transform for denoising. Then the dim image background is recovered and also carried out the gamma correction.
2.1. Denoising using Curvelet Transform
Curvelets are based on multiscale ridgelets combined with a spatial band pass filtering operation to isolate different scales. Like ridgelets, curvelets occur at all scales, locations, and orientations. However, while ridgelets all have global length and variable widths, curvelets in addition to a variable width have a variable length and so a variable anisotropy. The length and width at fine scales are related by a scaling law width = length2 and so the anisotropy increases with decreasing scale like a power law. Curvelet transform is the new member of the evolving family of multiscale geometric transforms. It offers an effective solution to the problems associated with image denoising using wavelets. [7,8,9,10].
2.2. Algorithm Steps to Denoising
Following steps are involved in the denoising algorithm of Curvelet Transform: 1. Compute all thresholds for cuvelets;
2. Compute norm of curvelets;
3. Apply curvelet transform to noisy image;
4. Apply hard thresholding to the curvelet coefficients; and
5. Apply inverse curvelet transform to the result of step 4 (to get its original image)
Fig1 shows the denoised image with curvelet transform. The denoised level in curvelet transform is better than wavelet transform.
(a) (b) Fig1 a) Noisy Dim Image b) Denoised image
2.3. Morphological Reconstruction
For a noisy dim image first removed the noise through curvelet transform. Now find out the image background (Dim image not have fine details). Fig 2 illustrates the image with poor lighting. Where f is the original image. The morphological reconstruction filters which enhance the contrast are used to get a background of image. In paper [5] explains the morphological filters such as opening by reconstruction and closing by reconstruction. This paper shows opening by reconstruction gives the satisfactorily result with poor lighting image.
Fig2.Uneven background of Image with poor lighting
2.4. Opening By Reconstruction
In mathematical morphology the class of transformations that allows the filtering of image without generating new components. They are called as opening by reconstruction. It touches regional minima and merges regional maxima. This characteristic allows the modification of the altitude of regional maxima when the size of the structuring element increases. This effect can be used to detect the background criteria. In opening by reconstruction, it won’t take extra details. It only takes the original details. Undesirable regions are eliminated without affecting the remaining structure of the image. It filters an image without generating new components. Here the structuring element size is 3x3.
Erodes an image and then dilates the eroded image using the same structuring element for both operations. Without generating new components this filters an image. It touches the regional minima and merges regional maxima. Extreme points and local information provided by original function is taken.
b(x) = (1)
Where erosion size µ=1.Substitute the opening by reconstruction operator (ie, opening followed by erosion shown in Fig3) instead of background criteria i.e. (1). This technique has no checking of background criteria with original image f.
To enhance the contrast in poor lighting is calculated by the following equation using (1) and (3).
(2)
Where k(x)= (3)
(a) (b)
Fig3.a) Opening by Reconstruction b) Erosion followed by Opening by Reconstruction
2.5. Closing By Reconstruction
Opening by reconstruction requires removing pixels of the foreground from an image by any given criteria and in reconstructing all connected components of the image that had not been totally removed. Closing by reconstruction is the dual of the opening by reconstruction. Fig 4 shows the operation of the closing by reconstruction. Here f denotes the original image function and φ(f) denotes the image after the closing by reconstruction was applied. We may use the Closing By Reconstruction filter instead of Opening By Reconstruction. It removes all unconnected dark regions in the enhanced image. The closing by reconstruction is used the background of image in (4).
.
Fig4.Closing By Reconstruction
We may use the Closing By Reconstruction filter instead of Opening By Reconstruction. It removes all unconnected dark regions in the enhanced image. The closing by reconstruction is used the background of image in (4).
(4)
The image in Fig6(b) shows the result of closing by reconstruction. It has a slight improvement compared with Fig5(b).
(a) (b)
Fig5. a) Denoised image b)Background detected image(using opening by reconstruction)
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Fig6. a)Denoised image b)Background detected image(using closing by reconstruction) 2.6. Gamma correction
The aim of gamma correction is to bright the dark regions and to maintain the bright regions as in original. In opening by reconstruction some regions are over illuminated. So before that to correct the illumination using the gamma functions.
3.CONCLUSION
The noisy dim image is denoised with the help of cuvelet transform which outperforms wavelet transform.After detect the background using opening by reconstruction which gives satisfactorily result in images with poor lighting.Then do the same dim image with closing by reconstruction. Compare the enhanced images. After that apply the gamma function for the purpose of illumination Correction.This paper gives the new method for enhance the noisy dim image and improve the quality of the image.
REFERENCES
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[3] David K.Hammond, Eero P.Simoncelli “ Image Denoising with an orientation-Adaptive Gaussian scale mixture scale model” IEEE proceedings on image processing, Atlanta,Georgia,Oct.8-11, 2006
[4] G. Jagadeeswar Reddy, T. Jaya Chandra Prasad, M.N. Giri Prasad “ Fingerprint Image Denoising using curvelet transform” ARPN Journal of Engineering and Applied Sciences,Vol.3, No.3, June 2008.
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Authors
Ms I.Muthu Selvi received her B.E degree in Computer Science and Engg in 2005 from Anna university Chennai and pursuing ME degree in Computer Science and Engineering in 2010 from Anna university Tirunelveli. Her areas of interest are Image processing, operating systems and Computer architecture. She has presented many papers in national Conferences in various fields. As part of this paper, she is working on developing morphological reconstruction filters for enhancing the images taken from poor lighting.
Ms J.Roselin received her B.E degree in Computer Science and Engg in 1999 from Madurai kamararaj University ME degree in Computer Science and Engineering in 2008 from Anna University Chennai. Currently she is Pursuing her PhD from Anna University Tirunelveli. She has published many papers in various fields. Her research area is Energy Efficient Routing in Wireless Sensor Networks. She is a member of ISTE.
Dr.V.Kavitha obtained her B.Edegree in Computer Science and Engg in 1996 from MS University and ME degree in Computer Science and Engineering in 2000 from Madurai Kama Raj University. She is the University Rank Holder in UG and Gold Medalist in PG.She received PhD degree in computer science and Engg from Anna University Chennai in 2009. Right from 1996 she is in the Department of Computer Science & Engg under various designations. Presently she is working as Asst. Prof in the Department of CSE at Anna University Tirunelveli..In addition She is the Director In-Charge of University V.O.C College of Engineering. Tuticorin. Currently, under her guidance ten
full time and part time. Her research interests are Wireless networks Mobile Computing, Network Security, Wireless Sensor Networks, Image Processing, Cloud Computing .She has published many papers in national and International journal in areas such as Network security, Mobile Computing, wireless network security, and Cloud Computing. She is a life time member of ISTE.
