Esteban Vera

Multiscale gigapixel photography

Pixel count is the ratio of the solid angle within a camera’s field of view to the solid angle covered by a single detector element. Because the size of the smallest resolvable pixel is proportional to aperture diameter and the maximum field of view is scale independent, the diffraction-limited pixel count is proportional to aperture area. At present, digital cameras operate near the fundamental limit of 1–10 megapixels for millimetre-scale apertures, but few approach the corresponding limits of 1–100 gigapixels for centimetre-scale apertures. Barriers to high-pixel-count imaging include scale-dependent geometric aberrations, the cost and complexity of gigapixel sensor arrays, and the computational and communications challenge of gigapixel image management. Here we describe the AWARE-2 camera, which uses a 16-mm entrance aperture to capture snapshot, one-gigapixel images at three frames per minute. AWARE-2 uses a parallel array of microcameras to reduce the problems of gigapixel imaging to those of megapixel imaging, which are more tractable. In cameras of conventional design, lens speed and field of view decrease as lens scale increases, but with the experimental system described here we confirm previous theoretical results suggesting that lens speed and field of view can be scale independent in microcamera-based imagers resolving up to 50 gigapixels. Ubiquitous gigapixel cameras may transform the central challenge of photography from the question of where to point the camera to that of how to mine the data.

Total variation approach for adaptive nonuniformity correction in focal-plane arrays

In this Letter we propose an adaptive scene-based nonuniformity correction method for fixed-pattern noise removal in imaging arrays. It is based on the minimization of the total variation of the estimated irradiance, and the resulting function is optimized by an isotropic total variation approach making use of an alternating minimization strategy. The proposed method provides enhanced results when applied to a diverse set of real IR imagery, accurately estimating the nonunifomity parameters of each detector in the focal-plane array at a fast convergence rate, while also forming fewer ghosting artifacts.

Fast adaptive nonuniformity correction for infrared focal-plane array detectors

A novel adaptive scene-based nonuniformity correction technique is presented. The technique simultaneously estimates detector parameters and performs the nonuniformity correction based on the retina-like neural network approach. The proposed method includes the use of an adaptive learning rate rule in the gain and offset parameter estimation process. This learning rate rule, together with a reduction in the averaging window size used for the parameter estimation, may provide an efficient implementation that should increase the original methods scene-based ability to estimate the fixed-pattern noise. The performance of the proposed algorithm is then evaluated with infrared image sequences with simulated and real fixed-pattern noise. The results show a significative faster and more reliable fixed-pattern noise reduction, tracking the parameters drift, and presenting a good adaptability to scene changes and nonuniformity conditions.

Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors

In this paper, we propose a novel algorithm for image reconstruction from compressive measurements of wavelet coefficients. By incorporating independent Laplace priors on separate wavelet sub-bands, the inhomogeneity of wavelet coefficient distributions and therefore the structural sparsity within images are modeled effectively. We model the problem by adopting a Bayesian formulation, and develop a fast greedy reconstruction algorithm. Experimental results demonstrate that the reconstruction performance of the proposed algorithm is competitive with state-of-the-art methods while outperforming them in terms of running times.

Iterative image restoration using nonstationary priors

In this paper, we propose an algorithm for image restoration based on fusing nonstationary edge-preserving priors. We develop a Bayesian modeling followed by an evidence approximation inference approach for deriving the analytic foundations of the proposed restoration method. Through a series of approximations, the final implementation of the proposed image restoration algorithm is iterative and takes advantage of the Fourier domain. Simulation results over a variety of blurred and noisy standard test images indicate that the presented method comfortably surpasses the current state-of-the-art image restoration for compactly supported degradations. We finally present experimental results by digitally refocusing images captured with controlled defocus, successfully confirming the ability of the proposed restoration algorithm in recovering extra features and rich details, while still preserving edges.

Static compressive tracking

This paper presents the Static Computational Optical Undersampled Tracker (SCOUT), an architecture for compressive motion tracking systems. The architecture uses compressive sensing techniques to track moving targets at significantly higher resolution than the detector array, allowing for low cost, low weight design and a significant reduction in data storage and bandwidth requirements. Using two amplitude masks and a standard focal plane array, the system captures many projections simultaneously, avoiding the need for time-sequential measurements of a single scene. Scenes with few moving targets on static backgrounds have frame differences that can be reconstructed using sparse signal reconstruction techniques in order to track moving targets. Simulations demonstrate theoretical performance and help to inform the choice of design parameters. We use the coherence parameter of the system matrix as an efficient predictor of reconstruction error to avoid performing computationally intensive reconstructions over the entire design space. An experimental SCOUT system demonstrates excellent reconstruction performance with 16X compression tracking movers on scenes with zero and nonzero backgrounds.

Coating the 8-m Gemini telescopes with protected silver

The Gemini telescopes were designed to be infrared-optimized. Among the features specified for optimal performance is the use of silver-based coatings on the mirrors. The feasibility study contracted by Gemini in 1994-1995 provided both techniques and recipes to apply these high-reflectivity and low-emissivity films. All this effort is now being implemented in our coating plants. At the time of the study, sputtering experiments showed that a reflectivity of 99.1% at 10μm was achievable. We have now produced bare and protected silver sputtered films in our coating plants and conducted environmental testing, both accelerated and in real-life conditions, to assess the durability. We have also already applied, for the first time ever, protected-silver coatings on the main optical elements (M1, M2 and M3) of an 8-m telescope. We report here the progress to date, the performance of the films, and our long-term plans for mirror coatings and maintenance.

Superresolution imaging in optical tweezers using high-speed cameras.

High-speed cameras are reliable alternatives for the direct characterization of optical trap force and particle motion in optical tweezers setups, replacing indirect motion measurements often performed by quadrant detectors. In the present approach, subpixel motion data of the trapped particle is retrieved from a high-speed low-resolution video sequence. Due to the richness structure of motion diversity of microscopic trapped particles, which are subjected to a Brownian motion, we propose to also use the obtained motion information for tackling the inherent lack of resolution by applying superresolution algorithms on the low-resolution image sequence. The obtained results both for trapping calibration beads and for living bacteria show that the proposed approach allows the proper characterization of the optical tweezers by obtaining the real particle motion directly from the image domain, while still providing high resolution imaging.

A novel iterative image restoration algorithm using nonstationary image priors

In this paper, we propose a novel algorithm for image restoration based on combining nonstationary edge-preserving priors. We develop a Bayesian modeling followed by an evidence analysis inference approach for deriving the foundations of the proposed iterative restoration algorithm. Simulation results over a variety of blurred and noisy standard test images indicate that the presented method outperforms current state-of-the-art image restoration algorithms. We finally present experimental results by digitally refocusing images captured with controlled defocus, successfully confirming the ability of the proposed restoration algorithm in recovering extra features and details, while still preserving edges.

Gigapixel Imaging with the AWARE Multiscale Camera

Gigapixel cameras have been confined to specialized applications such as aerial photography and astronomical observatories. A simplified architecture would better suit terrestrial imaging and reduce instrument cost and complexity. Our gigapixel AWARE camera is based on monocentric multiscale optical design principles that produce high-resolution images with a field of view (FOV) limited only by vignetting. This design allows resolution to approach the theoretical diffraction limits for a given entrance pupil size and FOV.