אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

In recent years, a number of super-resolution techniques have been proposed. Most of these techniques construct a high resolution image by either combining several low resolution images at sub-pixel misalignments or by learning correspondences between low and high resolution image pairs. These techniques and most other image interpolation methods focus on grayscale images. In this work, a new super-resolution method for color textures from a single image is presented. The interpolation process takes advantage of the repetitive nature of textures and the availability of several similar patches within them. In addition, it utilizes the color-intensity correlation that often exists in natural images, as well as local image characteristics such as smoothness and edges in the vicinity of the interpolated pixel. The extracted information is used in order to perform stochastic interpolation of the missing pixels, i.e., probability distributions are formed according to which the interpolated values are chosen. The advantage of this approach is its ability to maintain the statistical properties in the low resolution image, as well as its suitability to a broad class of textures. The interpolation results of the proposed method are shown to outperform presently available methods. Part of an MSc thesis under the supervision of Prof. Moshe Porat.