<p>The main part of this thesis is concerned with detailed explanation of a newly proposed Markov random field-based de-interlacing algorithm. Previous works, assume a first or higher-order Markovian spatial inter-dependency between the pixel intensity values. In accord with the specific interpolation problem in hand, they try to approximate the Markov random field parameters using available original pixels. Then using the approximate model, they define an objective function such as energy function of the MRF to be optimized. The efficiency and accuracy of the optimization step is as important as the effectiveness of definition of the cost (objective function) as well as the MRF model.\\ \indent The major concept that distinguishes the newly proposed algorithm with the aforementioned MRF-based models is the definition of the MRF not over the intensity domain but over interpolator (interpolation method) domain. Unlike previous MRF-based models which try to estimate a two-dimensional array of pixel values, this new method estimates an MRF of interpolation function (interpolators) associated with the 2-D array of pixel intensity values.\\ \indent With some modifications, one can utilize the proposed model in different related fields such as image and video up-conversion, view interpolation and frame-rate up-conversion. To prove this potential of the proposed MRF-based model, we extend it to an image up-scaling algorithm. This algorithm uses a simplified version of the proposed MRF-based model for the purpose of image up-scaling by a factor of two in each spatial direction. Simulation results prove that the proposed model obtains competing performance results when applied in the two interpolation problems of video de-interlacing and image up-scaling.</p> / Master of Applied Science (MASc)