Fuzzy Techniques for Image Enhancement
| dc.contributor.author | Kansal, Nitish Kumar | |
| dc.contributor.supervisor | Bala, Anju | |
| dc.date.accessioned | 2010-09-01T08:54:03Z | |
| dc.date.available | 2010-09-01T08:54:03Z | |
| dc.date.issued | 2010-09-01T08:54:03Z | |
| dc.description | ME | en |
| dc.description.abstract | The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide ‘better’ input for other automated image processing techniques. Fuzzy techniques can manage the vagueness and ambiguity efficiently (an image can be represented as fuzzy set). Fuzzy logic is a powerful tool to represent and process human knowledge in form of fuzzy if-then rules. The manipulation of these concepts leads to theory of approximation using fuzzy systems in image processing. In recent years, many researchers have applied the fuzzy logic to develop new image processing algorithms. The fuzzy image processing is one of the important application areas of fuzzy logic. In this thesis the algorithm is proposed using fuzzy membership function. This algorithm enhances image contrast very effectively. If the observed data are disturbed by random noise then the intensifier operator should convert the probabilistic data into fuzzy data. Some images are not available to good quality, so proposed fuzzy algorithm can be used for image enhancement to improve the quality of images. All the implementation work has been done in MATLAB 7.5 image processing tool box. Experimental results show that the quality of image is improved. | en |
| dc.description.sponsorship | CSED | en |
| dc.format.extent | 3767601 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10266/1215 | |
| dc.language.iso | en | en |
| dc.subject | Image Enhancement | en |
| dc.subject | Fuzzy Techniques | en |
| dc.title | Fuzzy Techniques for Image Enhancement | en |
| dc.type | Thesis | en |