Abstract
Recently, the invention of hand-held light field camera raises a revolution in photography. We can record not only the intensity of light but also the direction of light by the light field camera. With the additional information, we could process many applications such as digital refocusing, moving observer, and depth estimation which can be utilized to computer vision, computer graphics, and machine vision. In this thesis, we mainly concentrate on the function of digital refocusing which can easily get series of images with different focal length. We design an energy function and minimize it to get a label map which represents the index of sharpest image for each pixel. The primary core of the energy function is a pixel-based AFM method and a region-based adaboost classification method is secondary. We get a more robust result than traditional depth from focus (DFF) method through this energy function. We also generate a virtual all-focus image for further applications by utilizing the label map. We us the Lytro light field camera to capture real world scene and refocus it to a set which contains several images with different focal length. For each pixel, we compute the cost to each label by applying the energy function described above. Finally, we generate a label map and a virtual all-focus image by our algorithm.