The Design of Fuzzy Image Filters for Evolvable Hardware Environments
碩士 === 國立高雄大學 === 電機工程學系碩士班 === 100 === Denoising is to remove or reduce noises from the contaminated images and is an important research area in image processing. Denoising methods can work well if the noise models can be known in prior. However, it may lack of flexibility and adaptability when un-...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2012
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| Online Access: | http://ndltd.ncl.edu.tw/handle/64851617875256829433 |
| Summary: | 碩士 === 國立高雄大學 === 電機工程學系碩士班 === 100 === Denoising is to remove or reduce noises from the contaminated images and is an important research area in image processing. Denoising methods can work well if the noise models can be known in prior. However, it may lack of flexibility and
adaptability when un-modeled types of noises are encountered. Evolvable Hardware
(EHW) is a combination of evolutionary algorithm and reconfigurable hardware devices. EHW can change its architecture adaptively and produce flexible results for un-modeled problems. This study analyzes types of noises by fuzzy rules and builds EHW models for filtering image with various types of noises. Noise types are categorized and defined in fuzzy terms according to the similarity and divergence of the masked pixels in a sliding window. The value for restoring the noisy pixel is calculated from the outputs from the EHW models. With the proposed method, the efficiency of training EHW models and accuracy of image filtering are both improved. This thesis evaluates and compares the performance of the proposed method with other ones.
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