Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/18376
Title: | Classification-based Adaptive Image Denoising |
Authors: | McCrackin, Laura |
Advisor: | Shirani, Shahram |
Department: | Electrical and Computer Engineering |
Keywords: | Image denoising;Noise reduction;Seam energy;Seam carving;Saliency;Support vector machine;Classifier;Colour variance |
Publication Date: | Nov-2015 |
Abstract: | We propose a method of adaptive image denoising using a support vector machine (SVM) classifier to select between multiple well-performing contemporary denoising algorithms for each pixel of a noisy image. We begin by proposing a simple method for realistically generating noisy images, and also describe a number of novel and pre-existing features based on seam energy, local colour, and saliency which are used as classifier inputs. Our SVM strategic image denoising (SVMSID) results demonstrate better image quality than either candidate denoising algorithm for images of moderate noise level, as measured using the perceptually-based quaternion structural similarity image metric (QSSIM). We also demonstrate a modified training point selection method to improve robustness across many noise levels, and propose various extensions to SVMSID for further exploration. |
URI: | http://hdl.handle.net/11375/18376 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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mccrackin_laura_m_201509_masc.pdf | Thesis | 17.17 MB | Adobe PDF | View/Open |
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