7. Electronics - IJECEIERD -Image Denoising - Usha Rani - Unpaid

International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD) ISSN 2249-684X Vol.2, Issue 3 Sep 2012 64-74 Š TJPRC Pvt. Ltd.,

IMAGE DENOISING TECHNIQUES A COMPARATIVE STUDY 1

USHA RANI, 2CHARU NARULA & 2PARDEEP

1

Masters in Engg, Student of Electronics and Communication Engg, Deptt of UIET, Punjab University, Chandigarh, India 2,2

Faculty of Electronics and comm. Engg, Deptt of UIET, Punjab University, Chandigarh, India

ABSTRACT The growth of media communication industry and demand of high quality of visual information in modern age has open an interest to researcher to develop varies method of image denoising based on different best techniques. The visual information transmitted in form of image is naturally corrupted by Gaussian noise which is classical problem in image processing. This additive random noise can be removed using wavelet denoising technique due to the ability to capture the energy of a signal in few energy transform values. In this paper, an comparison has been made on suitability methods of image denoising to remove noise using different techniques. The performance of the image denoising is shown in terms of PSNR and visual performance. The result shown curvelet transform gave better PSNR and visual performance than wavelet transform and other methods.

KEYWORDS: PSNR (Peak Signal to Noise Ratio), DWT (Discrete Wavelet Transform).

INTRODUCTION The importance of the image denoising could be a serious task for medical imaging, satellite and areal image processing, robot vision, industrial vision systems, micro vision systems, space exploring etc. The noise is characterized by its pattern and by its probabilistic characteristics. There is a wide variety of noise types while we focus on the most important types, they are; Gaussian noise, speckle noise, poison noise, impulse noise, salt and pepper noise. A large number of linear and non linear filtering algorithms [3] have been developed to reduce noise from corrupted images to enhance image quality. Images may be corrupted by noise. Noise is present as a result of the electronic circuitry of cameras or in the image transmission period. The most common type of noise is the white or Gaussian noise where its power is uniformly distributed over the spectral and spatial spaces and its mean is zero.

Linear is an efficient technique to deal with additive noise while non linear filters are efficient to deal with the multiplicative and function based noise. Discrete Ridgelet and curvelet transforms are suited for the removal of noise. Till now the curvelet transform is counted best for denoising. In this paper, an comparison of different denoising techniques has been made in PSNR and visual quality of an image The performance of still image denoising is analyzed in terms of PSNR and visual artifact. while we focus on

65

Image Denoising Techniques a Comparative Study

the most important types, they are; Gaussian noise, speckle noise, poison noise, impulse noise, salt and pepper noise. Different techniques are used to denoise the image. Still image contains a large amount of spatial redundancy in plain areas where adjacent picture element (pixels, pels) have almost the same values which means the pixel values are highly correlated [1][2]. Ridgelet and curvelet transforms are suited for the removal of noise. Till now the curvelet transform is counted best for denoising. In this paper, an comparison of different denoising techniques has been made in PSNR and visual quality of an image. The performance of still image denoising is analyzed in terms of PSNR and visual artifact. While we focus on the most important types, they are; Gaussian noise, speckle noise, poison noise, impulse noise, salt and pepper noise. Different techniques are used to denoise the image.

SPATIAL FILTERS Spatial filters are direct and high speed processing tools of images. While the conversion is a must to obtain the spectral structure of an image the spatial filters stay as a preferable solution for almost all image processing techniques.

Gaussian Filter The 2D Gaussian has the mathematical form of equation and the distribution presented in figure (1)

Median Filter Median filter is a digital filtering technique used to remove noise from images or signals. The idea is to examine a sample of the input and decide if it is representative of the signal. This is performed using a window (local filtering) consisting of an odd number of samples. The values in the window are sorted into numerical order; the median value, the sample in the centre of the window, is selected as the output. The oldest sample is discarded, a new sample acquired, and the calculation repeats. Median filtering is a common step in image processing. It is particularly useful to reduce salt and pepper noise and speckle noise as well [12-13]. Its edge preserving nature makes it useful in cases where edge blurring is undesirable.

Usha Rani, Charu Narula & Pardeep

66

Adaptive Filter The Adaptive filter is a spatial filter whose response is based on ordering (ranking). The pixel contained in an image 246 neighborhood (local filtering) and then replacing the value of the center pixel in the neighborhood with the value determined by the ranking result adaptive filtering is a useful tool for reducing Gaussian and poison noise in an image. Its edge-preserving nature makes it useful in cases where edge blurring is undesirable.

DISCRETE WAVELET TRANSFORM Discrete wavelet transform of an image produces a non redundant image representation that provides better spatial and spectral localization of image formation, compared to other multi scale representation [5]. The Discrete Wavelet Transform (DWT) analysis, is based on the assumption that the amplitude rather than the location of the spectra of the signal to be as different as possible from the amplitude of noise. This allows clipping, thresholding, and shrinking of the amplitude of the coefficients to separate signals or remove noise. It is the localizing or concentrating properties of the discrete wavelet transform that makes it particularly effective when used with this nonlinear filtering method [6][7]. Wavelet transform uses hard thresholding and soft Thresholding for denoising.

Classical Wavelet-Based Denoising Methods Consist of Three Steps 1. Compute the discrete wavelet transforms (DWT).

2. Remove noise from the wavelet coefficients.

3. Reconstruct the enhanced image by using the

INVERSE DWT. Fuzzy logic based algorithm has been used for removal of noise. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which is suffering from shift variance. Decimated wavelet transform has been used for several reasons:1

The ability to compact most of the signals energy into a few transformation coefficients which is called energy compaction.

2

The ability to capture and represent effectively low frequency components as well as high frequency transients.

3

The variable resolution decomposition with almost uncorrelated coefficients.

67

Image Denoising Techniques a Comparative Study

CURVELET TRANSFORM The first-generation curvelet transform [12] [3] is based on the traditional structure. The process of this transform is: the image is pretreated, and then using ridgelet transform to the pretreated image. But this method has complex computational algorithm and limits the size of the image. Therefore, Candes brings forward a new framework system in 2003, now known as FDCT [2] [3]. FDCT is different from the first curvelet transform completely. It is realized in the field of FT. In the process of disposing image , in order to fit the Cartesian coordinate system of the image, we use the same center of the box instead The first-generation curvelet transform [12] [3] is based on the traditional structure. The process of this transform is: the image is pretreated, and then using ridgelet transform to the pretreated image. But this method has complex computational algorithm and limits the size of the image. Therefore, Candes brings forward a new framework system in 2003, now known as FDCT [12] [13]. FDCT is different from the first curvelet transform completely. It is realized in the field of FT. In the process of disposing image , in order to fit the Cartesian coordinate system of the image, we use the same center of the box instead. In the images, clear image features, such as straight lines, curves, contours and so on, is often

Usha Rani, Charu Narula & Pardeep

manifested in gray value and its changes, but it is reflected in the size factor in the curvelet domain, especially the details of the image perform a lot of high-frequency coefficients in the curvelet domain, that is to say: the larger high-frequency coefficients in the curvelet domain represent more information on texture.

Therefore, the curvelet transform can get sparser on the edge of the image than that in the wavelet transform. In the field of image processing, there are two FDCT algorithms, one is based on non-equivalent discrete Fast Fourier Transform algorithm, the other is based on the rapid Wrapping algorithm [15], these two algorithms have the same output, but the latter is faster and more efficient. In this article, we use the latter algorithm.

68

69

Image Denoising Techniques a Comparative Study

Curvelet transform has been developed to overcome the limitations of wavelet and Gabor filters. Though wavelet transform has been explored widely in various branches of image processing, it fails to represent objects containing randomly oriented edges and curves as it is not good at representing line singularities. Gabor filters are found to perform better than wavelet transform in representing textures and retrieving images due to its multiple orientation approach. However, due to the loss of spectral information in Gabor filters they cannot effectively represent images.

Curvelet transform is proposed by Candes and Donoho in 1999[1], its essence is derived from the ridge-wave theory [2-4]. In the foundation of single ridge-wave or local ridge-wave transform, we can construct Curvelet to express the objects which have curved singular boundary, Curvelet combines the advantages of ridge-wave which is suitable for expressing the lines’ character and wavelet which is suitable for expressing the points’ character and take full advantage of multi-scale analysis, it is suitable for a large class of image processing problems and has got quite good results in practical application.

Instead of a tilted grid, we assume a regular rectangular grid and define ‘Cartesian’ curvelets in essentially the same way as before, The discrete curvelt transform has formula is

Usha Rani, Charu Narula & Pardeep

70

Decomposition includes the following steps [6]: 1) Sub-band decomposition: Use wavelets transform to the image and decompose it to multiple sub-band components; 2) Smooth segmentation:” smooth segment” every sub- band to some sub-blocks, the size of the sub-blocks after each scale’s division can be determined according to specific needs and can be different each other.

3) Ridge-wave analysis: make localized ridge-wave transform to each sub-blocks after segmentation.

Curvelet transform has been used for the denoisng the medical images, grey scale images and color images. Higher PSNR and MSE with better visual quality curvelet transform becomes now a day’s most useful. Although wavelet transform has several advantages but the curvelet transform becomes more useful by competing other techniques. The curvelet transform for the lenna and Barbara images contaminated with noise gives the higher PSNR. Curvelet transform method has the phenomenon that it would appear slightly “scratches” and “ringing” in the image which is dialled with by denoising and reconstruction.

The image contains a variety of areas, such as texture region, smooth region and so on; these different areas have different tolerances to the noise. If we use the same processing method with no difference, the consequences are filtering too much detailed information and damaging the detailed information of the edge or the edges are protected but to retain too much noise or it may cause a variety of distortions.[7] based on Curvelet transform is proposed. Characteristic of Wavelet transform and traditional Curvelet transform, this method divides image up via window neighbourhood processing. It fuses two denoising images based Wavelet and Curvelet transform according to segmentation information and obtains higher quality and better effect image.

Curvelet transform have been used to denoise the CT, MRI medical images. It gives the best result in extracting the features. Curvelet transform is used for the extracting facial expression, denoising, image fusion, mainly for the reconstruction of the image.

71

Image Denoising Techniques a Comparative Study

Fig 2 Curvelet Transform The traditional Curvelet transform has the problem, has the phenomenon that it would appear slightly “scratches” and “ringing” in the image which is dialed with by denoising and reconstruction. The method to modify the curvelet transform has been used, in this an improved image denoising method.

Peak Signal to Noise Ratio Peak Signal to Noise Ratio PSNR is used to measure the difference between two images. The PSNR is given in decibel units (dB), which measure the ratio of the peak signal and the difference between two images. Mathematically it is defined as;

72

Usha Rani, Charu Narula & Pardeep

Fig. 3 Shows The Comparison Between Wavlet And Curvelet Transform Technique. (a) Original image (b) Noisy image (c) Two dimensional wavelet transform (d) Traditional curvelt transform (e) Segmentation image information (f) Improved curvelet transform

CONCLUSIONS Although spatial filters, Fourier transform, wavelet transform has their own significance in denoising an image. The purpose of Fourier transform is to convert a time domain signal into fourier domain. FT uses the Fourier analysis to measure the frequency components of the signal. Different

73

Image Denoising Techniques a Comparative Study

spatial filter are used to denoise the different noises contaminated with image. After this, Wavelet transform has been used for removal of different noise. But the wavelet transform has drawbacks in extracting the information from the edges, curves of the image. Wavelet transforms works well for the salt and pepper noise. To overcome this drawback of wavelet transform the curvelet transform has been proposed. The curvelet transform best suited for extracting the information at the edges and curves of an image. The curvelet transform works well to extract features of an image. From the literature survey, the curvelet transform has been proved best for denoising of an image but for the salt and pepper noise the curvelet transform does not works well. Different algorithms have been used to modify the curvelet transform. Hence the comparison of all denoising techniques shows the curvelet transform is best suited for denoising of a particular image.

REFERENCES 1.

Chaur-chin Chen, ‘On the Selection of Image Compression Algorithm’, Pattern Recognition, 1998, Fourteen International Conference, Vol 2, 16-20 Aug.

2.

Sonja Grgic, Mislav Grgic, ‘Performance Analysis of Image Compression Using Wavelets’, IEEE International Symposium on Industrial Electronics, Bled, Slovenia, Vol 43, No 3, June 2001.

3.

Rafael Gonzalez Richard Woods , Digital Image Processing, Pearson Publications.

4.

Jean-Luc Starck; Candes, E.J.; Donoho, D.L.;” The curvelet transform for image denoising” Volume: 11 , Issue: 6 , Publication Year: 2002 , Page(s): 670 – 684.

5.

Jiang Tao; Zhao Xin; Ding WenWen; Chen JunQing“Improved image denoising method based onCurvelet transform” . Publication Year: 2010 , Page(s): 1086 - 1090Information and Automation (ICIA), 2010 IEEE International Conference on

6.

Sylvain Durand, Jacques Froment, ‘Artifact Free Signal Denoising with Wavelets’, International on Acoustic, Speech and Signal Processing (ICASSP 2001), Salt Lake City, Utah (USA).

7.

Bui and G. Y. Chen, ‘Translation invariant denoising using multiwavelets’,IEEE on Signal Processing, Vol 46, no 12, pp.3414-3420, 1998.

8.

Yuan Ruihong; Tang Liwei; Wang Ping; Yao Jiajun ”Image Denoising Based on Curvelet Transform and Continuous Threshold”, Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on . Publication Year: 2010 , Page(s): 13 – 16

9.

Bhadauria, H.S.; Dewal, M.L. “Performance evaluation of curvelet and waveletbased denoising methods on brain Computed Tomography images”, Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on

10. Publication Year: 2011 , Page(s): 666 – 670.

Usha Rani, Charu Narula & Pardeep

74

11. Hong-qiao Li; Sheng-qian Wang; Cheng-zhi Deng “New Image Denoising Method Based Wavelet andCurvelet Transform”, Information Engineering, 2009. ICIE '09. WASE International Conference on . Volume: 1, Publication Year: 2009 , Page(s): 136 – 139.

12. Sumana, I.J.; Islam, M.M.; Dengsheng Zhang; Guojun Lu,”Content based image retrieval using curvelet transform”, Multimedia Signal Processing, 2008 IEEE 10th Workshop on 13. Publication Year: 2008, Page(s): 11 – 16.

14. LI Chuan-zhen, WANG Hui, WANG Jing-dong, ZHANG Lei. Curvelet Transform for Image Denoising Based on BayesShrink[J]. Digital TV and digital video, 2007.

15. LI Lin. SAR image enhancement algorithm, based on curvelet transform [J]. Chinese, Journal of Scientific Instrument, Jun, 2006.

16. CANDES E J, DONOHO D L. New tight, frames of curve lets and optimal, representations of objects with smooth, singularities [J]. Department of Statistics, Stanford University, Tech. Rep, 2002.

17. YANG Jia-hong, XU Can-hui, WANG, Yao-nan. Image de-noising algorithm based, on fast curvelet transform [J]. Computer Engineering and Applications, 2007, 43(6): 31- 33.

18. ZHANG Zhi-yu, BI Yang. Blind separation of noisy image based on the curvelet transform and independent component analysis [J]. Computer Applications, Feb, 2007.