AbstractIn this thesis we present an overview of sparse approximations of grey level images. The sparse representations are realized by classic, Matching Pursuit (MP) based, greedy selection
strategies. One such technique, termed Orthogonal Matching Pursuit (OMP), is shown to be suitable for producing sparse approximations of images, if they are processed in small blocks. When the blocks are enlarged, the proposed Self Projected Matching Pursuit (SPMP) algorithm, successfully renders equivalent results to OMP. A simple coding
algorithm is then proposed to store these sparse approximations. This is shown, under certain conditions, to be competitive with JPEG2000 image compression standard. An
application termed image folding, which partially secures the approximated images is then
proposed. This is extended to produce a self contained folded image, containing all the information required to perform image recovery. Finally a modified OMP selection technique is applied to produce sparse approximations of Red Green Blue (RGB) images.
These RGB approximations are then folded with the self contained approach.
|Date of Award||29 Nov 2013|
|Supervisor||Laura Rebollo-Neira (Supervisor)|
- orthogonal matching pursuit
- sparse approximations
- image folding
- greedy algorithms