Gray Scale Image Edge Detection and Reconstruction Using Stationary Wavelet Transform In High Density Noise Values N.Naveen Kumar 1, J.Kishore Kumar 2, A.Mallikarjuna 3, S.Ramakrishna 4 123 Research Scholar, Department of Computer Science, S.V.University, Tirupati 4 Professor Department of Computer Science, S.V.University, Tirupati Abstract: In this paper a new robust Stationary Wavelet Transform Filtering (SWTF) technique is proposed which removes the noise as high as possible in high density noise values, without blurring the fine edge details. This algorithm overcomes the practical limitations of Canny operator. The experimental work examined at various noise density levels. Heuristically, it has been discovered that the proposed algorithm is most efficient for edge detection at low noise density levels as well as high noise density levels. The simulation results were compared with classical edge detection techniques such as the sobel, prewitt, Laplacian and canny operators. Among all general edge detection techniques Canny edge detection technique is better. Hence in this paper the simulation results compared the canny edge detection PSNR and proposed SWT edge detection technique. Keywords: Edge detection, Stationary Wavelet Transform, Noise, De noise, PSNR Values 1. INTRODUCTION Edge detection is a procedure in which one can find the presence and location of edges constituted by sharp changes in color, intensity of an image [1]. The image brightness is depending up on the depth of discontinuities of an image, discontinuities in surface orientation, different material properties and variations in scene. So it is a difficult task to remove the noise without eliminating the sharp characteristics of the image, such as edges, corners and other sharp structures during the de-noising process [2]. Edge detection is 89
susceptible to noise. This is due to the fact that the edge detection algorithms are designed to respond to sharp changes, which are caused by noisy pixels. There are several edge detection methods such as Sobel, Roberts and Laplacian. However, these gradient-based and zero-crossing finding algorithms are very sensitive to noise. These methods may underestimate the noise points as the part of real edges and miss some real edges because of the noise interference. Furthermore, the masks sizes are fixed and cannot be dilated for the need of various problem domains. Performed by multiple signal passage through the wavelet filter. When processing a 2-D image, the wavelet analysis is performed separately for the horizontal and the vertical function [3]. Edge detection has been used extensively in areas related to image and signal processing its use includes pattern recognition, image segmentation and scene analysis [4, 5]. Many classical edge detectors have been proposed like sobel, prewitt, laplacian and canny operators [6]. To reduce the influence of noise, many techniques have been proposed [7]. A new filtering technique proposed to identify the edges using discrete wavelet transform in [8]. Edges characterize boundaries and therefore a problem of fundamental importance in image processing. Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. To find fine results a new wavelet based edge detection algorithm presented [9]. To avoiding the inherent difficulties in classical Fourier analysis, a sliding window technique proposed in [9].A wavelet maxima de- noising based filtering technique is presented in [10]. Wavelet has many advantages compared to classical approaches in image processing because of effective noise handling. The wavelet transform is a comparatively new and fast developing method for analyzing signals. 2. EDGE DETECTION USING WAVELETS The main purpose and benefits of applying the wavelet transform for the detection of edges in an image is the possibility of choosing the size of the details that will be detected. The number of edges expected to get is set by the wavelet scale. In the case of the discrete wavelet transform, the choice of the scale is performed by multiple signal way through the wavelet filter. When processing a 2-D image, the 90
wavelet analysis is performed separately for the horizontal and the vertical directions. Thus, the vertical and the horizontal edges are detected separately. The 2D SWT decomposes the images into sub-images, 3 details and 1 approximation. 3. PROPOSED SWT EDGE DETECTION BLOCK DIAGRAM 4. Proposed SWT Edge Detection Algorithm A new two-stage cascaded filter is proposed which removes the noise as high as possible, without blurring by retaining the fine edge details. The proposed method relies on enhancing edges, taking the 91
advantage of the spatial coincidence of the local maxima at different scales. This algorithm processes the corrupted images by first detecting the impulse noise. The processing pixel is checked whether it is noisy or noisy free. That is, if the processing pixel lies between maximum and minimum gray level values then it is noise free pixel, it is left unchanged. If the processing pixel takes the maximum or minimum gray level then it is noisy pixel. The experimental work performed by examining edges at various scales. Heuristically, it has been discovered that the most efficient for edge detection at low noise density and high noise density. The flow chart in figure 1 summarizes the proposed algorithm. Referring to this figure, four band-pass components are obtained from SWT at each scale. After normalization, the point wise maximum across all of the four sub-bands is evaluated. Since the same window is uncorrelated, therefore, taking the maximum value pixel per pixel permits avoiding Salt and Pepper noise as much as possible. The same operation is performed until the window size W=5. Then, the difference between intermediate maxima previously calculated is combined by taking the median value. After applying inverse SWT (ISWT) the image reconstructed the original image with sharp edge points. 4.1 PROPOSED ALGORITHM Step1: Read the input image and convert it into GRAY scale image. Step2: Add salt & Pepper noise to the GRAY scale image Step3: Apply Stationary Wavelet Transform (SWT) to the image. Step4: SWT decomposes the images into sub-images, 3detail and 1 approximation (LL, LH, HL, and HH). Step 5: Select 2-D window of size 5 X 5. Step 6: Apply soble, prewit, Roberts and Canny operators to selected window. Step 7: Apply inverse SWT to reconstruct the original image. Step 8. If selected window W!=5 then go to step 4 until all the pixels process in the selected image. 92
5. RESULTS AND DISCUSSION Figure 2: SWT edge detection Figure 2 demonstrates how the image is decomposed in to four band widths in identify the edge detection. The original image reconstructed using SWT shown in figure 2. The edge detection results are visualized in figure 4 also compares the results of the proposed (SWT ) method with the well known general edge detection methods like Sobel, Prewitt, Laplacian and Canny edge detectors. In this case, a standard pepper image is used to evaluate the performance of the algorithm. It can be seen that most of the edge points are successfully, reconstructed using the classical techniques as well as the proposed technique. Figure 3: SWT edge detection at noise density 0.1 with the PSNR value as 44.822 93
6.COMPARISONS Among all general edge detection techniques Canny edge detection technique is better. Hence in the following table1 we compared the canny edge detection PSNR and SWT edge detection PSNR. In high noise density as well as low noise densities the SWT edge detection reconstructing original image better than the canny edge detection. 94
ta Figure5: Comparison of PSNR values between Canny Edge detection and SWT edge detection 7. CONCLUSION In this paper, analyses the various edge detectors on images corrupted with salt and pepper noise. If images are noise free then Canny and Laplacian and Log edge detection is better. We represented a new algorithm which will perform denoise as well as detect the edges properly compare with traditional edge detection methods. Canny edge detection method performing well and good in identifying the edges in low noise density values, but their performance is poor on smooth natural gray scale images at all noise levels. All edge detectors show poor performance quantitatively on all noisy images at noise variance/density (0.1 to 0.9). The proposed SWT based edge detection algorithm is performing better edge detection in high noise values References 1. I. Pitas and A. N. Venetsanopoulos, Nonlinear mean filters in image processing, IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP- 34, no. 3, pp. 573-584, June IY86 2. H. Hwang and R. A. Haddad, Adaptive Median Filters: New Algorithms and Results, IEEE 95
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