49843608-Complete-thesis-Report-merged

53 output pixels 8x8 124 124 124 125 125 124 124 124

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5.3 Output pixels (8x8) : 124 124 124 125 125 124 124 124 124 125 125 125 125 124 124 123 123 125 126 126 126 125 124 124 124 126 126 126 126 126 124 123 124 126 126 127 127 126 124 123 125 126 126 127 127 127 126 126 126 126 127 127 127 127 126 126 126 127 127 127 127 127 126 126 Table 5.2 output pixels of the FIS system.
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46 5.4 Image enhancement using Thresholding . Output of thresholding: Figure 5.2 Output of image enhancement using Thresholding .
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47 5.5 Spatial Average low pass filtering Figure 5.3 Output of image using Spatial Average Filtering .
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48 5.6 Histogram Equalization Figure 5.4. Histogram Equalization.
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49 5.7 Frequency Domain Image filters 1. Gaussian Low-pass Filter Figure 5.5 Output of the image using Gaussian Low- pass Filter.
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50 Figure 5.6 Output of all the methods.
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51 Chapter -6 Conclusion and Future Scope 6.1 Conclusion In this thesis work a technique is designed based on Fuzzy Inference System tool in MATLAB 7.5 ( MATLAB is a software package developed by Math Works).To enhance the pixels three membership functions are used. One can also use any automatic approaches for image enhancement to increase the image quality. One of the disadvantages of measure theory is the computational complexity if the number of elements is large. The proposed technique used fuzzy if then rules are a sophisticated bridge between human knowledge on the one side and the numerical framework of the computers on the other side, simple and easy to understand. To achieve a higher level of image quality considering the subjective perception and opinion of the human observers. The proposed technique is able to overcome the draw backs of spatial domain methods like thresholding and frequency domain methods like Gaussian low pass filter. The proposed technique is able to improve the contrast of the image. The proposed technique is tested on different type of images, like degraded, low contrasted images.
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52 6.2 Future Scope In the future the existing systems can be modified by fuzzy set theory application. Modification of fuzzy rules can produce better results. Neuro-Fuzzy techniques can be used to enhance the images.
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53 References [1] Farzam Farbiz, Mohammad Bager Menhaj, Seyed A. Motamedi, and Martin T.Hagan " A New Fuzzy Logic Filter for Image Enahcement" IEEE Transections Systems, Man, and Cybernetics-part B: Cyberntics, Vol.30,NO.1, February 2000. [2] Om Parkas Verma, Madasu Hanmandlu, Anil Singh Pariah and Vamp Krishna Madasu " Fuzzy Filter for Noise Reduction in Color Images" ICGST-GVIP Journal, Volume 9, September 2009, and ISSN:1687-398X. [3] Rafael C.Gonzalez and Richard, E. Woods " Digital Image Processing ‖ Third Edition- 2009. [4] Aboul Ella Hassanien,Amr Badr " A Comparative Study on Digital Mamography Enhancement Algorithms Based on Fuzzy Theory" Studies in Informatics and Control, Vol.12,No.1, March 2003. [5] Alper Pasha "Morphological image processing with fuzzy logic" Havaclilik ve uzay teknolojilerl derglsl ocak 2006 cilt 2 sayi 3 ( 27-34).
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