Linear reconstruction of compensated images: theory and experimental results

A Novel Dermaseptin Isolated from the Skin Secretion of Phyllomedusa tarsius and Its Cationicity-Enhanced Analogue Exhibiting Effective Antimicrobial and Anti-Proliferative Activities

A novel dermaseptin peptide, dermaseptin-PT9 (DPT9), was isolated and identified from Phyllomedusa tarsius by the combination of molecular cloning and LC-MS analysis. Chemically synthesised DPT9 was broadly effective against the tested microorganisms through the disruption of cell membranes and showed weak haemolytic activity towards horse erythrocytes. It also exhibited anti-proliferative effect against various human cancer cells. Moreover, an analogue with enhanced cationicity, K^{8, 23}-DPT9, in which Asp⁸ and Glu²³ were substituted by lysine residues, had a markedly increased antimicrobial effect against all tested microorganisms and disrupted microbial cell membranes. This analogue also showed no haemolysis at its effective antimicrobial concentrations. In addition, K^{8, 23}-DPT9 displayed an enhanced anti-proliferative effect against cancer cells, while displayed weak activity against the normal human cell line, HMEC-1.

Phase and amplitude imaging from noisy images by Kalman filtering

We propose and demonstrate a computational method for complex-field imaging from many noisy intensity images with varying defocus, using an extended complex Kalman filter. The technique offers dynamic smoothing of noisy measurements and is recursive rather than iterative, so is suitable for adaptive measurements. The Kalman filter provides near-optimal results in very low-light situations and may be adapted to propagation through turbulent, scattering, or nonlinear media.

Signal, noise, and bias for a broadband, division-of-amplitude Stokes polarimeter

We analyze estimation error as a function of spectral bandwidth for division-of-amplitude (DoAm) Stokes polarimeters. Our approach allows quantitative assessment of the competing effects of noise and deterministic error, or bias, as bandwidth is varied.We use the signal-to-rms error (SRR) as a metric. Rather than calculating the SRR of the estimated Stokes parameters themselves, we use the singular-value decomposition to calculate the SRRs of the coefficients of the measured data vector projected onto the measurement matrix left singular vectors.We argue that calculating the SRRs for left singular vector coefficients will allow development of reconstruction filters to minimize Stokes estimation error. For the example case of a source with constant polarization over a relatively wide band, we show that as the spectral filter bandwidth is increased to include wavelengths significantly different than the design wavelength, the SRRs of the estimated left singular vector coefficients will a.) increase monotonically if relatively few photo-detection events (PDEs) are recorded, b.) after a sharp peak close to the design wavelength, decrease monotonically if relatively many PDEs are recorded, and c.) have well-defined maxima for nominal PDE counts. Given some idea of the source brightness relative to detector noise, one can specify a spectral filter bandwidth minimizing the variance and bias effects and optimizing Stokes parameter estimation. Our approach also allows one to specify the bandwidth over which the response of "achromatic" optics must be reasonably invariant with wavelength for rms Stokes estimation error to remain below some desired maximum. Finally, we point out that our method can be generalized not only to other types of polarimeters, but also to any sensing scheme that can be represented by a linear system for limiting values of a certain parameter.

Evaluation of least-squares phase-diversity technique for space telescope wave-front sensing

Because of mechanical aspects of fabrication, launch, and operational environment, space telescope optics can suffer from unforeseen aberrations, detracting from their intended diffraction-limited performance goals. We give the results of simulation studies designed to explore how wave-front aberration information for such near-diffraction-limited telescopes can be estimated through a regularized, low-pass filtered version of the Gonsalves (least-squares) phase-diversity technique. We numerically simulate models of both monolithic and segmented space telescope mirrors; the segmented case is a simplified model of the proposed next generation space telescope. The simulation results quantify the accuracy of phase diversity as a wave-front sensing (WFS) technique in estimating the pupil phase map. The pupil phase is estimated from pairs of conventional and out-of-focus photon-limited point-source images. Image photon statistics are simulated for three different average light levels. Simulation results give an indication of the minimum light level required for reliable estimation of a large number of aberration parameters under the least-squares paradigm. For weak aberrations that average a 0.10lambda pupil rms, the average WFS estimation errors obtained here range from a worst case of 0.057lambda pupil rms to a best case of only 0.002lambda pupil rms, depending on the light level as well as on the types and degrees of freedom of the aberrations present.

Model-based image reconstruction by means of a constrained least-squares solution

The problem of the optimal use of object model information in image reconstruction is addressed. A closed-form solution for the estimated object spectrum is derived with the Lagrange multiplier technique, which assumes a measured image, knowledge of the optical transfer function, statistical information about the measurement noise, and a model of the object. This reconstruction algorithm is iterative in nature because the optimal Lagrange multiplier is not generally known at the start of the problem. We derive the estimator, describe one technique for determining the optimal Lagrange multiplier, demonstrate a stopping criterion based on the mean-square error between a noise-free image and the photon-limited version of the image, and show representative results for both filled- and sparse-aperture imaging applications.

Noise reduction in adaptive-optics imagery with the use of support constraints

The use of support constraints for noise reduction in images obtained with telescopes that use adaptive optics for atmospheric correction is discussed. Noise covariances are derived for these type of data, including the effects of photon noise and CCD read noise. The effectiveness of support constraints in achieving noise reduction is discussed in terms of these noise properties and in terms of the types of algorithms used to enforce the support constraint. Both a convex-projections and a cost-function minimization algorithm are used to enforce the support constraints, and it is shown with the use of computer simulations and field data that the cost-function algorithm results in artifacts in the reconstructions. The convex-projections algorithms produced mean-square-error decreases in the image domain of approximately 10% for high light levels but essentially no error decreases for low light levels. We emphasize images that are well resolved by the telescope and adaptive-optics system.