Image reconstruction is the process of converting a sampled image into a continuous one prior to transformation and resampling. This reconstruction can be more accurate if two things are known: the process by which the sampled image was obtained and the general characteristics of the original image. We present a new reconstruction algorithm known as Constraint-Based Interpolation, which estimates the sampling functions found in cameras and analyzes properties of real world images in order to produce quality real-world image magnifications. To accomplish this, Constraint-Based Interpolation uses a sensor model that pushes the pixels in an interpolation to more closely match the data in the sampled image. Real-world image properties are ensured with a level-set smoothing model that smooths "jaggies" and a sharpening model that alleviates blurring. This thesis describes the three models, their methods and constraints. The effects of the various models and constraints are also shown, as well as a human observer test. A variation of a previous interpolation technique, Quad-based Interpolation, and a new metric, gradient weighted contour curvature, is presented and analyzed.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
Goggins, Daniel David, "Constraint-Based Interpolation" (2005). Theses and Dissertations. 610.
computer, computer vision, image processing, magnification, graphics, image understanding