Difference images quantify changes in the object scene over time. In this paper, we use the feature-specific imaging paradigm to present methods for estimating a sequence of difference images from a sequence of compressive measurements of the object scene. Our goal is twofold. First is to design, where possible, the optimal sensing matrix for taking compressive measurements. In scenarios where such sensing matrices are not tractable, we consider plausible candidate sensing matrices that either use the available a priori information or are nonadaptive. Second, we develop closed-form and iterative techniques for estimating the difference images. We specifically look at l 2 - and l 1 -based methods. We show that l 2 -based techniques can directly estimate the difference image from the measurements without first reconstructing the object scene. This direct estimation exploits the spatial and temporal correlations between the object scene at two consecutive time instants. We further develop a method to estimate a generalized difference image from multiple measurements and use it to estimate the sequence of difference images. For l 1 -based estimation, we consider modified forms of the total-variation method and basis pursuit denoising. We also look at a third method that directly exploits the sparsity of the difference image. We present results to show the efficacy of these techniques and discuss the advantages of each

We introduce a new technique, spectral contrast imaging microscopy (SCIM), for super-resolution microscopic imaging. Based on a novel contrast mechanism that encodes each local spatial frequency with a corresponding optical wavelength, SCIM provides a real-time high-resolution spectral contrast microscopic image with superior contrast. We show that two microscopic objects, separated by a distance smaller than the diffraction limit of the optical system, can be spatially resolved in the SCIM image as different colors. Results with numerical simulation and experiments using a high-resolution United States Air Force target are presented. The ability of SCIM for imaging biological cells is also demonstrated.

Intrigued by our recent finding that the nuclear refractive index is significantly increased in malignant cells and histologically normal cells in clinical histology specimens derived from cancer patients, we sought to identify potential biological mechanisms underlying the observed phenomena. The cell cycle is an ordered series of events that describes the intervals of cell growth, DNA replication, and mitosis that precede cell division. Since abnormal cell cycles and increased proliferation are characteristic of many human cancer cells, we hypothesized that the observed increase in nuclear refractive index could be related to an abundance or accumulation of cells derived from cancer patients at a specific point or phase(s) of the cell cycle. Here we show that changes in nuclear refractive index of fixed cells are seen as synchronized populations of cells that proceed through the cell cycle, and that increased nuclear refractive index is strongly correlated with increased DNA content. We therefore propose that an abundance of cells undergoing DNA replication and mitosis may explain the increase in nuclear refractive index observed in both malignant and histologically normal cells from cancer patients. Our findings suggest that nuclear refractive index may be a novel physical parameter for early cancer detection and risk stratification.

Definitive diagnosis of malignancy is often challenging due to limited availability of human cell or tissue samples and morphological similarity with certain benign conditions. Our recently developed novel technology-spatial-domain low-coherence quantitative phase microscopy (SL-QPM)-overcomes the technical difficulties and enables us to obtain quantitative information about cell nuclear architectural characteristics with nanoscale sensitivity. We explore its ability to improve the identification of malignancy, especially in cytopathologically non-cancerous-appearing cells. We perform proof-of-concept experiments with an animal model of colorectal carcinogenesis-APC(Min) mouse model and human cytology specimens of colorectal cancer. We show the ability of in situ nanoscale nuclear architectural characteristics in identifying cancerous cells, especially in those labeled as "indeterminate or normal" by expert cytopathologists. Our approach is based on the quantitative analysis of the cell nucleus on the original cytology slides without additional processing, which can be readily applied in a conventional clinical setting. Our simple and practical optical microscopy technique may lead to the development of novel methods for early detection of cancer.

In this work we study the unique problems associated with resolving the direction of arrival (DOA) of coherent signals separated by less than an antenna beamwidth when the data are collected in the beamspace domain with, for example, electronically or holographically scanned antennas. We also propose a technique that is able to resolve these coherent signals. The technique is based on interpolation of the data measured by an element-space virtual array. Although the data are collected in the beamspace domain, the coherence structure can be broken by interpolating multiple shifted element-space virtual arrays. The efficacy of this technique depends on a fundamental tradeoff that arises due to a nonuniform signal-to-noise ratio (SNR) profile across the elements of the virtual array. This profile is due to the structure imposed by the specific beam pattern of the antenna. In addition to describing our technique and studying the SNR profile tradeoff, we also incorporate a strategy for improving performance through a subswath technique that improves covergence of covariance estimates.