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Filter for Removal of Impulse Noise By Using Fuzzy Logic
Er. Harish Kundra, Er. Monika Verma, Er. Aashima
Pages - 195 - 202     |    Revised - 15-06-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
Digital Image Processing (DIP), Image Enhancement (IE), Fuzzy Logic (FL), Peak-signal-tonoise- ratio (PSNR)
Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This paper combines the features of Image Enhancement and fuzzy logic. This research problem deals with Fuzzy inference system (FIS) which help to take the decision about the pixels of the image under consideration. This paper focuses on the removal of the impulse noise with the preservation of edge sharpness and image details along with improving the contrast of the images which is considered as the one of the most difficult tasks in image processing.
CITED BY (6)  
1 Archana, S., & Chabra, A. Performance Evaluation of SIHF and Contrast and Saturation Enhancement Based Denoising Techniques for Natural Images.
2 Kamya, S., & Sachdeva, M. (2013). Fuzzy Logic based Image De-noising and Enhancement for Grayscale Images. International Journal of Computer Applications, 74(2), 5-9.
3 Song, Y., Han, Y., Oh, J. S., & Lee, S. (2013). Edge Preserving Impulse Noise Reduction. Journal of Imaging Science and Technology, 57(6), 60507-1.
4 Priya, R., & Shanmugam, T. N. (2013). A comprehensive review of significant researches on content based indexing and retrieval of visual information. Frontiers of Computer Science, 7(5), 782-799.
5 Mehta, S., & Dhull, S. fuzzy based median filter for gray-scale images.
6 Mahakale, S. R., & Thakur, N. V. (2007). A Comparative Study of Image Filtering on Various Noisy pixels. image, 17.
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Dr. Er. Harish Kundra
- India
Dr. Er. Monika Verma
- India
Dr. Er. Aashima
- India

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