AN EFFICIENT EDGE DETECTION APPROACH USING DWT

Transcription

1 International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 5, September-October 2018, pp , Article ID: IJCET_09_05_005 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication AN EFFICIENT EDGE DETECTION APPROACH USING DWT A.H.M. Jaffar Iqbal Barbhuiya Department of Computer Science, Assam University, Silchar, India ABSTRACT In today s digital world, image edge detection is widely used in all over the globe. Digital image edge detection is used to detect the sharp changes in brightness of the images and the edge of a digital image. There are many classical techniques used to detect image edge detection like Canny, Sobel, Prewitt and Robert edge detector method. The proposed research work mainly focuses on the study of different edge detection methods which were previously derived and to propose an efficient method of digital image edge detection technique using Discrete Wavelet Transform (DWT). The results were compared with one of the best edge detector method: Canny Edge Detector. In the proposed image edge detection method, the first analytical result showed that an input image after applying the thresholding technique represents a segmentation of image edges and the second analytical results showed the 8- connected components of pixel connectivity in two dimensional images related to their neighbor pixels producing the enhanced edges. To evaluate the performance of the proposed algorithm with the existing algorithms, the two thresholding values were applied as T_low and T_high using hysteresis thresholding method. PSNR and width σ were also computed to detect the efficiency of the proposed algorithm. Key words: Connectivity analysis, DWT, Edge detection, PSNR, Thresholding. Cite this Article: A.H.M. Jaffar Iqbal Barbhuiya, An Efficient Edge Detection Approach using DWT. International Journal of Computer Engineering and Technology, 9(5), 2018, pp INTRODUCTION An edge of an object in an image gives the border or boundary of a particular object in an image that differentiates adjacent image regions. Digital image edge detection is used to detect the sharp changes in brightness of the images and the edge of a digital image. So, in digital image processing, an edge is a curve which ensures a path of significant changes in image intensity [1]. There are many classical techniques used to detect image edge detection like Canny, Sobel, Prewitt and Robert edge detector method. This research work is organized as follows: in section II, a review of the edge detection methods are described, in section III, the proposed edge detection approach using Canny edge detector and DWT is presented, in section IV, evaluation results as well as discussions of the 32 editor@iaeme.com

2 An Efficient Edge Detection Approach using DWT proposed algorithm is explained. In section V, the performance analysis of the proposed algorithm with various existing algorithm is shown. In section VI, the conclusive remark of the proposed work is discussed. 2. RELATED WORK Edges in the image intensity can be broadly divided into two parts [1]. (i) Step Edges- where the image intensity suddenly changes from one value on one side of the discontinuity to a different value on the opposite side. (ii) Line Edges- where the image intensity suddenly changes value but again back to the initial value within a short distance. Step edges become ramp edges and line edges become roof edges but the intensity changes in between them are not instantaneous. The edge detection process used to simplify the image analysis to reduce the amount of data to be processed. Step edge detection used to improve as the operator point spread function is extended along with the edge. In Canny edge detection scheme, different operators were used at each point. The directional operator outputs were integrated with the gradient maximum detector [2]. The goal of edge detection is the detection and characterization of significant changes in the images. A method to solve such kind of edge detection problem was introduced by Torre and Poggio [3] where they described the three main objectives of edge detection: (i) The properties of different types of filters, (ii) Relationships among various types of 2-D differential operators and (iii) Geometrical and topological properties of the zero crossings of differential operators. In this method, they used Morse theory and compared the algorithm with the recent results on edge detection. A new algorithm was proposed which can detect edges to the various parameters from the local maxima based on wavelet transform and mostly focused on the solution of image processing and computer vision complications [4]. They also proposed a coding algorithm which identifies the most significant image edges to acquire a compact representation. But they have not used any edge enhancement technique for thinning the edges. The wavelet and its application in image edge detection was described using Gaussian function as the smoothing function and the results were compared with zero crossings and Sobel operators [5]. But they neither discussed any comparison with Canny and nor applied any enhancement technique for thinning the edges. Another new wavelet-based approach to solve the edge detection problem was reported where the authors adopted the method of Canny s three criteria to derive wavelet-based edge filter and to detect edge points of an image efficiently and accurately at different scales[6]. Continuous wavelet transform was converted to discrete form and an optimization method has been introduced in the edge filter derivation process. They compared the performance of the algorithm with Mallat-Zhong s edge detection method. But in their method, they have not used any algorithm for thinning of image edges. A discrete expression of Canny s criteria for step edge detection was developed. The existence of two classes of derivative operators and those classes exhibit different properties for good localization and low responses multiplicity were also introduced. The performance of the method also compared with the existing algorithms [7]. A stationary wavelet edge detection algorithm was proposed and the results were tested by adding noises in the images [8]. The Pratt s Figure of Merit (FOM) algorithm was also introduced to analyze the performance of the simulation results and the edge images were compared with traditional edge detection methods. In another work, four problems of edge detection have been introduced along with the solutions: (i) The simultaneous detection of all step edges from a fine to a coarse scale, (ii) The edge detection of thin bars with a width of very few pixels, (iii) The detection of trihedral junctions and (iv) The development of an algorithm with an image independent parameters. The solutions of these problems were 33 editor@iaeme.com

3 A.H.M. Jaffar Iqbal Barbhuiya combined with an extensive spatial filtering with classical methods of computer vision and newly developed algorithm [9]. An advanced wavelet based technique for edge detection was proposed which applied spatial domain method and the simulation results were compared with the classical edge detection techniques. They also introduced two new algorithms for detecting the edges. The first algorithm was called RC algorithm and the second algorithm was called RCD algorithm [10]. Canny s edge detection enhancement by scale multiplication function was defined as the responses of the detection filter at two scales. Edge maps were also constructed as the local maxima by thresholding the scale multiplication results [11]. A comparative analysis of various image edge detection techniques were presented by [12] which showed that the Canny s edge detection algorithm performs better than all other classical edge detection methods. They also observed that the Canny s edge detection algorithm is more costly as compared to LoG, Sobel, Prewitt and Robert s operators. An edge detection approach based on directional wavelet transform was proposed by [13] which retains the separable filtering and the simplicity of computations and filter design from the standard 2-D wavelet transform. An edge detection method using magnetic resonance images was introduced by [14] using 2-D DWT. The method tested with various wavelet functions both on simulated and real medical images and the results were compared with basic edge detection methods including gradient operators and Canny edge detector. A novel edge detection algorithm for noisy images was proposed by [15] and it was found that it works efficiently in both the noisy and real images. The proposed edge detection method was compared with respect to PSNR value and Gaussian noise. The results were found that the proposed method performs better than the classical edge detectors. A modern wavelet based edge detection technique has been introduced by [16] and compared the simulation results with Fourier transform and discrete cosine transform and the method was demonstrated for iris imagery. A modern robust stationary wavelet transform was introduced by [17] for detecting the grayscale image edges and reconstruction of images using stationary wavelet transform in high density noise values. The proposed results were compared with some existing edge detection techniques such as Canny, Sobel, Prewitt and Laplacian operators. Edge detection using wavelets for noisy images both theoretically and experimentally was discussed and compared the results with traditional edge detection techniques on images like lena [18]. A new edge detection algorithm was proposed by [19] based on wavelet transform and Canny edge detection method and the results were compared with some previous traditional edge detection methods. An unsupervised multiresolution image segmentation algorithm that combines wavelet transform and FCM clustering considering the neighbor pixels was introduced [20]. The daubechies wavelet transform has been used by [21] and the 2-D image has decomposed at three levels and for each level lower and higher frequencies were separated to get the appropriate edge for black and white images. 2-D images have been tested in Matlab by using wavelet transform of real objects. An image processing method was applied by [22] to execute edge detection with the standard operators-based edge detection techniques. The method also described the computation of histogram to obtain all the number of peaks and threshold values. They have shown that after applying wavelet edge detection method to the segmented images, which generated through their preprocessing approach, gave the better performance as compared to the traditional edge detection techniques. A new algorithm was developed to show the comparison of edge detection techniques showing the advantages and disadvantages of the existing algorithms. A novel edge detection algorithm was proposed by [23] using 2-D DWT and the results were compared with one of the best classical edge detector: Canny Edge detector. The simulation results were shown that in presence of noise it 34 editor@iaeme.com

4 An Efficient Edge Detection Approach using DWT can extract information and perform edge detection method very easily. a new approach based on applying stationary wavelet transform (SWT) and FCM algorithm for segmentation of brain MRI images and compared the proposed method with the previous techniques was discussed [24]. An automatic approach for detecting the EEG signals of non-focal and focal groups was proposed [25]. The EEG signal is fragmented into rhythms using empirical wavelet transform method. The attributes taken out from the rhythms of EEG signals produced an accuracy of 90% using 50 pairs of EEG signals. To evaluate the performance of the algorithm, the proposed method compared with some other existing algorithms which identify focal EEG signals naturally. A novel tool wear monitoring system based on the monitored edge detection was proposed [26]. The method fragments the tool edge with morphological component analysis. The method also minimizes the impact of texture and noise for edge detection and enhanced the coherence and connectivity of edges. The consequences shown better geometrical accuracy and reduced error rate in the tool estimation method. An edge based image compression algorithm in F-transform domain was proposed [27]. Various input image blocks taken into consideration as low, medium and high intensity blocks based on image edges obtained using Canny edge detection algorithm. These three blocks are compressed using F- transform and then Huffman coding is applied on compressed blocks to reduce the bit rate. The evaluation measures considered to analyze the performance of the proposed algorithm. A hybrid edge detection method of brain tumor using FCM clustering and discrete curvelet transform was proposed [28]. The objective of their work was to reduce the amount of noise using feature extraction. The data points fitted into more than one cluster centers and finally combining curvelet transform, FCM and wavelet transform were applied on the input images and measured the performance of the proposed algorithm using SNR, PSNR, MSE and SSIM. A new edge detection method based on single pixel imaging in the frequency domain was introduced [29]. They introduced two methods as SCHEME-I and SCHEME-II to describe the proposed algorithm. In SCHEME-I, special sinusoidal structures for the x-directional edge and y-directional edge of the unknown objects are designed. The frequency spectrum for the edge then acquired using a four-step phase-shifting method with the designed sinusoidal structures in a single pixel imaging system. In SCHEME-II, the frequency spectrum of the unknown objects initially acquired followed by the frequency spectrum of edge acquired by calculations. The output edges are finally acquired by inverse Fourier transform based on frequency spectrum. Simulation results showed that the proposed method able to construct high quality character edges and the image objects. 3. PROPOSED EDGE DETECTION MODEL USING DWT The following steps are required to implement the proposed algorithm. In this proposed image edge detection model, when we consider an input image as color image then it will go for pre-processing and converted to gray scale image to reduce the redundant or color image data. If the input image is a gray scale image then it will directly go for image decomposition. 1-level 2-D DWT is applied to create LL subband from the original image since the LL subband holds most of the information of an image and also it gives assistance to minimize the bandwidth of an image. 2-D DWT is applied to the LL subband of the gray level image in all image rows as well as in all image columns as horizontal gradient edges and vertical gradient edges editor@iaeme.com

5 A.H.M. Jaffar Iqbal Barbhuiya Figure 1 Block Diagram of the proposed algorithm The image Decomposition using Wavelet Transform (DWT) is shown below. Figure 2 Discrete Wavelet Transform (DWT) based 1-Level Image Decomposition As soon as we determine the degree of the robustness of the edges based on gradient magnitudes, the next step is to apply a threshold to resolve certainly that edges are detected or not at an image point. If we consider the lower threshold value then the chances of detection is more and the consequences of the result will be progressively vulnerable to noise and it also detects the edges of insignificant features in the image. If the edge detection process is applied to only the gradient magnitude image, then its output edges become broader in size and hence edge thinning algorithm for post-processing is necessary to detect the sharp edges. In this proposed research work, we apply an edge detection approach to manage the obstacle of choosing proper thresholds for thresholding using hysteresis method. This technique employs multiple thresholds to detect image edges. We initiate the process by applying the upper level threshold value to detect the beginning of an edge. When we detect the beginning point of an edge, we are then able to detect the passage of the edge through the image pixel by pixel and labeling an edge if the lower threshold is higher than the limit. When the value drops below the lower threshold value then the edge labeling is stopped. This method builds the presumption that edges are represented in continuous curves and allows us to pursue an imperceptible section of an edge. Sometimes it may be difficult to select the appropriate parameters and suitable thresholding value which may vary over the image. So, the 8-components pixel connectivity analysis algorithm for edge thinning is applied to detect the thin image edges and after the edges have been flatten by applying a proper threshold value. The conventional 8-components pixel connectivity algorithm works as follows [30]: 36 editor@iaeme.com

6 An Efficient Edge Detection Approach using DWT 1. Select a type of pixel to pixel connectivity computation process as 8-connected components. 2. In 8-connected components connectivity, 8 immediate pixels surrounding a particular pixel completely connected horizontally, vertically and diagonally is considered. 3. Remove the points from the four main directions clockwise. 4. Apply the same process in multiple passes, subsequent to north pass; apply the identical sub-processed image in the other passes and so on. 5. Eliminate a point if the point has no adjacent pixels in the north. 6. Eliminate a point if the point is not the end of a sentence. 7. Eliminate a point if the point is detached. 8. Eliminate a point if eliminating the points will not occur to isolate with its nearest pixels arbitrarily. 9. Otherwise retain the point. The steps required to execute the program is shown below Proposed Algorithm Step 1: Read an input image, I Step 2: Apply pre-processing of the input image, I if the image is a color image or else go for DWT decomposition.. Step 3: Apply 1 level DWT based on Haar wavelet on the pre-processing image or the gray scale input image to acquire LL subband. Step 4: Again, apply gradient edges both horizontally and vertically in the LL subband of the input image. Step 5: Apply Hysteresis Thresholding as T_low and T_high to detect multiple edges. Step 6: Finally, apply the conventional 8-components connectivity analysis of pixels to achieve thinner edges described in the above section of the proposed research work. 4. RESULTS AND DISCUSSION The proposed algorithm utilizes edge information extracted using Canny edge detector. The thresholding method is used to minimize the number of false edges in the nonmaximal suppression of gradient magnitude. All the values which are below the standard thresholding level are changed to zero. But still some false edges may arise because of the threshold value T which is too small in number and some portion of actual edges may be lost due to the thresholding value T, if it is too high. Choosing the proper threshold value is difficult and time consuming and integrates hit or miss [27]. In general, more successful thresholding method uses two thresholding values that is, T_low and T_high. In Original images, at first the Canny edge detector is applied and then on the extracted gray scale image DWT is applied with threshold T_low=0.001, T_high=0.1 and width σ=0.5, T_low=0.005, T_high=0.2 and width=0.5 and T_low=0.40, T_high=0.50 with width σ=1, out of five outputs results, only one is depicted in this proposed research work which is shown in figure 9. It is seen that an increase in the value of threshold decreases the number of truly detected edges. To measure the performance of the proposed algorithm, PSNR value of each images are computed and it has been tested on five set of test images: peppers, lenna, airplane, Barbara and house of size 256X256 and 512X512. The test images are collected from SIPI image database editor@iaeme.com

7 A.H.M. Jaffar Iqbal Barbhuiya Table 1 Qualitative measures of PSNR for 5 input images using the proposed algorithm Images Resolution Method PSNR (db) 512X512 Canny Edge Detector Peppers.jpg 512X512 DWT+ Canny Edge Detector (Proposed) X512 Canny Edge Detector Lena.jpg 512X512 DWT+ Canny Edge Detector (Proposed) X512 Canny Edge Detector Airplane.jpg 512X512 DWT+ Canny Edge Detector (Proposed) X512 Canny Edge Detector Barbara.jpg 512X512 DWT+ Canny Edge Detector (Proposed) X256 Canny Edge Detector House256.jpg 256X256 DWT+ Canny Edge Detector (Proposed) Here, the output shows a color image converted to gray scale image and after applying the thresholding technique which represents a segmentation of image and also shows the pixel connectivity in two dimensional images related to their neighbor pixels. The Edge Detection of an image is detected, analyzed and filtered using 8-neighbour pixel connectivity analysis and the output images are compared with one of the best classical image edge detection method called Canny edge detector [2]. The implementation of results shows that in a grayscale image DWT is applied firstly, then after applying the thresholding on that decomposed image enhances the edges and finally, after applying the 8-neighbour connectivity analysis, the edges become thinner as the unwanted points on the edges are removed. Fig. 3 depicts the original Lena image decomposed using DWT at level 1 decomposition along with the reconstructed image after decomposition. Fig. 4 depicts the original Lena grayscale image decomposed using DWT at level 1 decomposition along with the reconstructed image after decomposition. Fig. 5 depicts the proposed grayscale image of Lena after applying thresholding technique as well as after applying 8-components connectivity analysis. In fig. 6, Raw 1 represents the original images of Lena, Peppers and Airplane. Raw 2 represents the Canny edge detected images of Raw 1 at threshold T=0.001 and Raw 3 represents the proposed edge detection method at Threshold T_low=0.001 and T_high=0.1 with σ=0.5. Table 2 Qualitative measures of T_low, T_high and σ for 3 input images using the proposed algorithm Images T_low T_high σ PSNR Peppers.jpg Lenna.jpg Airplane.jpg editor@iaeme.com

8 An Efficient Edge Detection Approach using DWT Figure 3 a) Original Lena.jpg image, b) Decomposed image and c) Reconstructed image using 1 Level DWT Figure 4 a) Original Lena.jpg grayscale image, b) Decomposed image and c) Reconstructed image using 1 Level DWT Image after applying thresholding Image after applying connectivity analysis Figure 5 a) Lena.jpg image after applying thresholding and b) Lena.jpg image after applying 8- components connectivity analysis 39 editor@iaeme.com

9 A.H.M. Jaffar Iqbal Barbhuiya Figure 6 Row 1: Original images of Lenna, Peppers and Airplane, Row 2: Canny Edge Detected images at threshold, T= 0.001, and Row 3: Proposed Edge Detection Method at Threshold T_low=0.001 and T_high=0.1 with σ= PERFORMANCE ANALYSIS From the visual representation of output images and the PSNR values of various output images, it has been observed that the proposed method is better as compared to Canny edge detection algorithm because of the proposed DWT features and its various thresholding values. Table 3 Performance table of edge detection results Authors Method PSNR Kumar et al., (2011) SWT Edge Bhadauria and Singh (2013) Wavelet and Canny Edge detector (noisy image) Gambhir and Rajpal (2017) Canny Edge detector and F-Transform Mohammed and Katran (2018)Curvelet Transform Proposed Method DWT and Canny edge detector CONCLUSIONS In this research work, a study has been carried out on digital image edge detection method and proposed a hybrid edge detection algorithm using Canny edge detector and Discrete Wavelet Transform (DWT) and two techniques are applied to enhance the image edges; one is called hysteresis thresholding and another one is called 8-component pixel connectivity. Both visual and analytical computational measures represents that the proposed algorithm outperforms better as compared to existing methods. In the future, the proposed work may be additionally expanded to provide an edge detection method using quantum computing, Continuous Wavelet Transform and Complex Wavelet Transform (CWT). REFERENCES [1] D. Ziou and S. Tabbone, Edge detection techniques-an overview, Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii, 8, 1998, [2] J. Canny, A computational approach to edge detection, IEEE Transactions on pattern analysis and machine intelligence, 1986, [3] V. Torre and T.A Poggio, On edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, [4] S. Zhong and S. Mallat, Compact image representation from multiscale edges, IEEE Proceedings of Third International Conference on Computer Vision, Dec 4, 1990, [5] L. Deren and S. Juliang, The wavelet and its application in image edge detection, ISPRS journal of photogrammetry and remote sensing, 49(3), Jun 1, 1994, editor@iaeme.com

10 An Efficient Edge Detection Approach using DWT [6] J.W Hsieh, M.T Ko, H.Y Liao and K.C Fan, A new wavelet-based edge detector via constrained optimization, Image and Vision Computing, 15(7), Jul 1, 1997, [7] D. Demigny and T. Kamle, A Discrete Expression of Canny, IEEE Transactions on Pattern Analysis & Machine Intelligence. 1(11), Nov, 1997, [8] S. Nashat, A. Abdullah and M.Z. Abdullah, A stationary wavelet edge detection algorithm for noisy images, 2004, C. and R. E. Woods, Digital Image Processing. [9] F.A Pellegrino, W. Vanzella and V. Torre, Edge detection revisited, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(3), Jun, 2004, [10] Y.S. Al-halabi and H.J. Abd, New wavelet-based techniques for edge detection, threshold, 2005, 8-9. [11] P. Bao, L. Zhang and X. Wu, Canny edge detection enhancement by scale multiplication, IEEE transactions on pattern analysis and machine intelligence, Sep, 2005, 27(9), [12] R. Maini and H. Aggarwal, Study and comparison of various image edge detection techniques, International journal of image processing (IJIP), 3(1), Jan, 2009, 1-1. [13] Z. Zhang, S. Ma H. Liu and Y. Gong, An edge detection approach based on directional wavelet transform, Computers & Mathematics with Applications, 57(8), Apr, 2009, [14] J. Petrova and E. Hostalkova, Edge detection in medical image using the Wavelet transform, Report of Research, Department of Computing and Control Engineering, Czech Public, [15] G.K. Srivastava, R. Verma, R. Mahrishi and S. Rajesh, A novel wavelet edge detection algorithm for noisy images, ICUMT'09, IEEE International Conference on Ultra Modern Telecommunications & Workshops, Oct 12, 2009, 1-8. [16] R.K. Dehankar, S.C. Bhivgade and A.R. Khan, Wavelet Based Edge Detection Technique for Iris Recognition Using MATLAB 1. [17] N.N. Kumar, J.K. Kumar, A. Mallikarjuna and S. Ramkrishna, Gray Scale Image Edge Detection and Reconstruction using Stationary Wavelet Transform in High Density Noise Value. International Journal of Computer Engineering & Applications, II (III), 2013, [18] N.V. An, Edge detection using Wavelets, VNU Journal of Science, Natural Sciences and Technology, 29(2), May [19] H.S. Bhadauria and A. Singh, Wavelet and Canny based Edge Detection Method for Noisy Lung CT Image, International Journal of Emerging Technology and Advanced Engineering (IJETAE), 3(5), May 2013, [20] Y. Shi, Y. Gu, L.L. Wang and X.C. Tai, A fast edge detection algorithm using binary labels, [21] K. Kumar, N. Mustafa, J.P. Li, R.A. Shaikh, S.A. Khan and A. Khan, Image edge detection scheme using wavelet transform, 11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP),Chengdu, 2014, doi: /iccwamtip [22] M. Abo-Zahhad, R.R. Gharieb, S.M. Ahmed and A.A. Donkol, Edge detection with a preprocessing approach, Journal of Signal and Information Processing, 5(04), Oct 7, 2014, 123. [23] Monika and Renu Bala, Image Edge Detection using Discrete Wavelet Transform, International Journal of Innovative Research in Computer and Communication Engineering, 4(4), August, editor@iaeme.com

11 A.H.M. Jaffar Iqbal Barbhuiya [24] M. Eido, H.Massoud, B. Shanwar and N. Zarka, MRI image segmentation using stationary wavelet transform and FCM algorithm, HIAST, Damascus, [25] A. Bhattacharyya, M. Sharma, R.B. Pachori and P. Sircar, U.R. Acharya, A novel approach for automated detection of focal EEG signals using empirical wavelet transform. Neural Computing and Applications, 29(8), Apr 1, 2018, [26] X. Yu, X. Lin, Y. Dai and K. Zhu, Image edge detection based tool condition monitoring with morphological component analysis, ISA transactions, Jul 1, 2017, 69, [27] D. Gambhir and N. Rajpal, Edge and fuzzy transform based image compression algorithm: Edgefuzzy, In Artificial Intelligence and Computer Vision 2017, Springer, Cham, [28] H.R. Mohammed and L.F. Katran, Hybrid Method for Detection of Brain Tumor Using Fuzzy C-Mean Clustering and Discrete Curvelet Transform, International Journal of Applied Engineering Research, 13(3), 2018, [29] H. Ren, S. Zhao and J. Gruska, Edge detection based on single-pixel imaging, Optics express, 26(5), Mar , [30] T.Y. Zhang and C.Y. Suen, A fast parallel algorithm for thinning digital patterns, Communications of the ACM, 27(3), Mar 1, 1984, editor@iaeme.com