Digital Image Processing using Local Segmentation

A unifying philosophy for carrying out low level image processing called “local segmentation” is presented. Local segmentation provides a way to examine and understand existing algorithms, as well as a paradigm for creating new ones. Local segmentation may be applied to range of important image processing tasks. Using a traditional segmentation technique in intensity thresholding and a simple model selection criterion, the new FUELS denoising algorithm is shown to be highly competitive with state-of-the-art algorithms on a range of images. In an effort to improve the local segmentation, the minimum message length information theoretic criterion for model selection (MML) is used to select between models having different structure and complexity. This leads to further improvements in denoising performance. Both FUELS and the MML variants thereof require no special user supplied parameters, but instead learn from the image itself. It is believed that image processing in general could benefit greatly from the application of the local segmentation methodology.