Evolving Image Noise Filters through Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Banks:2009:HPCMP-UGC,
  author =       "Edwin Roger Banks and Paul Agarwal and 
                 Marshall McBride and Claudette Owens",
  title =        "Evolving Image Noise Filters through Genetic
                 Programming",
  booktitle =    "DoD High Performance Computing Modernization Program
                 Users Group Conference (HPCMP-UGC), 2009",
  year =         "2009",
  month =        "15-18 " # jun,
  pages =        "307--312",
  abstract =     "A form of Evolutionary Computation (EC) called Genetic
                 Programming (GP) was used to automatically discover
                 sequences of image noise filters to remove two types of
                 image noise and a type of communications noise
                 associated with a remotely sensed imagery. Sensor noise
                 was modelled by the addition of salt-and-pepper and
                 grayscale noise to the image. Communication noise was
                 modeled by inserting a series of blank pixels in
                 selected image rows to replicate dropped pixel segments
                 occurring during communication interruptions of
                 sequential uncompressed image information.

                 A known image was used for training the evolver. Heavy
                 amounts of noise were added to the known image, and a
                 filter was evolved. (The filtered image was compared to
                 the original with the average image-to-image pixel
                 error establishing the fitness function.). The evolved
                 filter derived for the noisy image was then applied to
                 never-before-seen imagery affected by similar noise
                 conditions to judge the universal applicability of the
                 evolved GP filter. Examples of all described images are
                 included in the presentation.

                 A variety of image filter primitives were used in this
                 experiment. The evolved sequences of primitives were
                 each then sequentially applied to produce the final
                 filtered image.

                 These filters were evolved over a typical run length of
                 one week each on a small Linux cluster. Once evolved,
                 the filters were then transported to a PC for
                 application to the never-before-seen images, using an
                 evolve-once, apply-many-times approach. The results of
                 this image filtering experiment were quite dramatic.",
  keywords =     "genetic algorithms, genetic programming, Linux
                 cluster, communication interruptions, communications
                 noise, evolutionary computation, grayscale noise, image
                 filtering, image noise filters, remotely sensed
                 imagery, salt-and-pepper noise, sensor noise,
                 sequential uncompressed image information, Linux,
                 filtering theory, image denoising, image resolution,
                 image segmentation, image sequences",
  DOI =          "doi:10.1109/HPCMP-UGC.2009.50",
  notes =        "COLSA Corp., Huntsville, AL, USA Also known as
                 \cite{5729481}",
}

Genetic Programming entries for Edwin Roger Banks Paul Agarwal Marshall McBride Claudette Owens

Citations