New Approaches Based on Real and Complex Forms of Ripplet-I Transform for Image Analysis
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The multi resolution analysis are important parts of image processing. Curvelet transform is analysis method which have been using wide variety of applications in multi resolution analysis. Ripplet-I transform is defined by recently generalising of the curvelet transform by adding parameters support (c) and degree (d). Even though this transform has been found out recently, it has been using wide variety of applications. Fast discrete and complex fast discrete versions of ripplet-I transform were examined by this study. In denoising application, better results were obtained with fast discrete and complex fast discrete versions of ripplet-I transform by discrete ripplet-I transform. © 2016 IEEE.
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