By Topic

Adaptive Context-Tree-Based Statistical Filtering for Raster Map Image Denoising

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Minjie Chen ; Sch. of Comput., Univ. of Eastern Finland, Joensuu, Finland ; Mantao Xu ; Franti, P.

Filtering of raster map images is chosen as a case study of a more general class of palette-indexed images for the denoising problem of images with a discrete number of output colors. Statistical features of local context are analyzed to avoid damage to pixel-level patterns, which is frequently caused by conventional filters. We apply a universal statistical filter using context-tree modeling via a selective context expansion capturing those pixel combinations that are present in the image. The selective context expansion makes it possible to use a much larger spatial neighborhood, with a feasible time and memory complexity, than fixed-size templates. We improve the existing context-tree approaches in two aspects: Firstly, in order to circumvent the context contamination problem, a context-merging strategy is applied where multiple similar contexts are considered in the conditional probability estimation procedure. Secondly, we study a specific continuous-input-finite-output problem in which the map images are corrupted by additive Gaussian noise. Performance comparisons with competitive filters demonstrate that the proposed algorithm provides robust noise filtering performance and good structure preservation in all test cases without any a priori information on the statistical properties of the noise.

Published in:

Multimedia, IEEE Transactions on  (Volume:13 ,  Issue: 6 )