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PROJECT TITLE :

Translation Invariant Directional Framelet Transform Combined With Gabor Filters for Image Denoising - 2014

ABSTRACT:

This paper is dedicated to the study of a directional lifting transform for wavelet frames. A nonsubsampled lifting structure is developed to maintain the interpretation invariance as it is an necessary property in image denoising. Then, the directionality of the lifting-primarily based tight frame is explicitly mentioned, followed by a particular translation invariant directional framelet transform (TIDFT). The TIDFT has 2 framelets ?1, ?a pair of with vanishing moments of order two and one respectively, that can detect singularities during a given direction set. It provides an efficient and sparse illustration for images containing made textures together with properties of quick implementation and excellent reconstruction. Additionally, an adaptive block-wise orientation estimation method based on Gabor filters is presented instead of the conventional minimization of residuals. Furthermore, the TIDFT is utilized to use the capability of image denoising, incorporating the MAP estimator for multivariate exponential distribution. Consequently, the TIDFT is ready to eliminate the noise effectively whereas preserving the textures simultaneously. Experimental results show that the TIDFT outperforms another frame-based denoising strategies, such as contourlet and shearlet, and is competitive to the state-of-the-art denoising approaches.


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Image Representation Image Denoising Wavelet Transforms Exponential Distribution Gabor Filters Maximum Likelihood Estimation Directional Lifting Tight Wavelet Frame Translation Invariance Gabor Filter
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