
Be the first to like this
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
This paper proposes a neural network based region classification technique that classifies
regions in an image into two classes: textures and homogenous regions. The classification is
based on training a neural network with statistical parameters belonging to the regions of
interest. An application of this classification method is applied in image denoising by applying
different transforms to the two different classes. Texture is denoised by shearlets while
homogenous regions are denoised by wavelets. The denoised results show better performance
than either of the transforms applied independently. The proposed algorithm successfully
reduces the mean square error of the denoised result and provides perceptually good results.
Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.
Be the first to comment