Multi-dimensional Image Reconstruction Research Articles

Optical sensing and detection in turbid water using multidimensional integral imaging

We present an approach for optical sensing and detection in turbid water using multidimensional spatial-temporal domain integral imaging and dedicated signal processing algorithms. An optical signal is encoded using pseudorandom sequences, and an image sensor array is used to capture elemental image video sequences of light propagating through turbid water. Using the captured information, multidimensional image reconstruction followed by multi-dimensional correlation to detect the source signal is performed. We experimentally demonstrate scenarios in which turbidity causes conventional signal detection to fail, while our proposed multidimensional approach enables successful detection under the same turbidity conditions. Statistical analysis is provided to support the experimental results. To the best of our knowledge, this is the first report of using multidimensional integral imaging signal detection in turbid water conditions.

Taylor series expansion based multidimensional image reconstruction for confocal and 4pi microscopy

We propose and experimentally demonstrate a three-dimensional (3D) image reconstruction methodology based on Taylor series approximation (TSA) in a Bayesian image reconstruction formulation. TSA incorporates the requirement of analyticity in the image domain, and acts as a finite impulse response filter. This technique is validated on images obtained from widefield, confocal laser scanning fluorescence microscopy and two-photon excited 4pi (2PE-4pi) fluorescence microscopy. Studies on simulated 3D objects, mitochondria-tagged yeast cells (labeled with Mitotracker Orange) and mitochondrial networks (tagged with Green fluorescent protein) show a signal-to-background improvement of 40% and resolution enhancement from 360 to 240 nm. This technique can easily be extended to other imaging modalities (single plane illumination microscopy (SPIM), individual molecule localization SPIM, stimulated emission depletion microscopy and its variants).

Multidimensional image reconstruction in astronomy

The goal of this article is to describe a unified approach based on the assumption of additive Gaussian noise and regularization theory that illustrates the flavor of such multidimensional image reconstruction problems and the associated challenges. Efficient iterative methods are described for the solution of these problems. Indeed, a crucial point in the solution to the problems considered in this article is the size of the data set and the need for efficient algorithms.

Image Reconstruction and Multidimensional Field Estimation From Randomly Scattered Sensors

Many important problems in statistical signal processing can be formulated as function estimation from randomly scattered sensors in a multidimensional space, e.g., image reconstruction from photon-limited images and field estimation from scattered sensors. We present a novel approach to the study of signal reconstruction from random samples in a multidimensional space. In particular, we study a classical iterative reconstruction method and demonstrate that it forms a sequence of unbiased estimates for band-limited signals, which converge to the true function in the mean-square sense. We subsequently rely on the iterative estimation method for multidimensional image reconstruction and field estimation from sensors scattered according to a multidimensional Poisson and uniform distribution. Computer simulation experiments are used to demonstrate the efficiency of the iterative estimation method in image reconstruction and field estimation from randomly scattered sensors.