Image Restoration using Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Asmatullah Chaudhry",
  author2 =      "Asmat Ullah",
  title =        "Image Restoration using Machine Learning",
  school =       "Ghulam Ishaq Khan Institute of Engineering Sciences \&
  year =         "2007",
  address =      "Topi, NWFP, Pakistan",
  month =        mar,
  email =        "",
  keywords =     "genetic algorithms, genetic programming, Image
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "112 pages",
  abstract =     "Restoration of degraded images has become an important
                 and effective tool for many technological applications
                 like space imaging, medical imaging and many other
                 post-processing techniques. Most of the image
                 restoration techniques model the degradation phenomena,
                 usually blur and noise, and then obtain an
                 approximation of the image. Whereas, in realistic
                 situation, one has to estimate both the true image and
                 the blur from the degraded image characteristics in the
                 absence of any a priori information about the blurring
                 system. The objective of this thesis is to develop a
                 new punctual kriging based image restoration approach
                 using machine-learning techniques. To achieve this
                 objective, the research concentrates on the restoration
                 of images corrupted with Gaussian noise by making good
                 tradeoffs between two contradicting properties;
                 smoothness versus edge preservation.

                 This thesis makes the following contributions: (1)
                 Quantitative analysis of the at hand punctual kriging
                 based image restoration technique is carried out, (2)
                 Fuzzy logic, punctual kriging and fuzzy averaging are
                 used intelligently to develop a better image
                 restoration technique, (3) A new image quality measure
                 is proposed in terms of the semi-variograms to judge
                 the performance of image restoration techniques, (4)
                 Analysis of the effect of neighbourhood size on
                 negative weights and the subsequent improvement in
                 punctual kriging based image restoration is performed,
                 (5) To avoid both the problems of matrix inversion
                 failure and the negative weights in punctual kriging,
                 artificial neural network is used to develop a
                 neuro-fuzzy filter for image denoising, (6) Further,
                 using genetic programming, a hybrid technique for image
                 restoration based on fuzzy punctual kriging is
                 developed, the developed machine learning technique
                 uses local statistical measures along with kriged
                 information for subsequent pixel estimation. Main
                 parameters considered for evaluation of the proposed
                 technique are image quality measure and computational
                 cost. The image quality measures used for evaluation
                 and comparison include MSE, PSNR, SSIM, wPSNR, VMSE and
                 VPSNR. A series of empirical investigations have been
                 made to evaluate the performance of the proposed
                 techniques using database of standard images that show
                 the effectiveness of our methodology.",
  notes =        "Author also given as Ullah, Asmat eg by
                 Engineering & Technology (e) > Engineering(e1) >
                 Computer Sciences & related disciplines(e1.9) ID Code:

                 Supervisor: Anwar M. Mirza",

Genetic Programming entries for Asmatullah Chaudhry