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Center for Nonlinear Studies |
Over the last two decades, model-based imaging techniques have emerged as a principled framework for understanding and solving many of the most important problems in imaging research. The approach of model-based imaging is to construct a model of both the image and the imaging system, and then to use this integrated model to either reconstruct an unknown image, or to estimate unknown parameters. So for example, model-based image reconstruction and parameter estimation can be used to robustly form images from sensors with uncertain calibration. But in addition, model-based imaging can serve as a framework for optimizing the static and dynamic design of imaging sensor systems themselves. In this talk, we review some techniques and recent successes in model-based imaging. Two application domains that we consider are tomographic reconstruction from multislice helical-scan CT and electron microscopy, two very different sensors that share much in common when viewed from the perspective of model-based imaging. For both cases, we discuss a variety of technical innovations, which either improve image quality or reduce the computational burden. We then show results, which demonstrate the value of the methods both quantitatively and qualitatively, on a variety of real and simulated datasets. Finally, we conclude with a philosophical discussion of the future potential of model-based methods, and we present some emerging ideas in prior modeling of images, which have potential to substantially improve upon current results.