Sebastián E. Godoy

Noise-cancellation-based nonuniformity correction algorithm for infrared focal-plane arrays

The spatial fixed-pattern noise (FPN) inherently generated in infrared (IR) imaging systems compromises severely the quality of the acquired imagery, even making such images inappropriate for some applications. The FPN refers to the inability of the photodetectors in the focal-plane array to render a uniform output image when a uniform-intensity scene is being imaged. We present a noise-cancellation-based algorithm that compensates for the additive component of the FPN. The proposed method relies on the assumption that a source of noise correlated to the additive FPN is available to the IR camera. An important feature of the algorithm is that all the calculations are reduced to a simple equation, which allows for the bias compensation of the raw imagery. The algorithm performance is tested using real IR image sequences and is compared to some classical methodologies.

High operating temperature interband cascade focal plane arrays

In this paper, we report the initial demonstration of mid-infrared interband cascade (IC) photodetector focal plane arrays with multiple-stage/junction design. The merits of IC photodetectors include low noise and efficient photocarrier extraction, even for zero-bias operation. By adopting enhanced electron barrier design and a total absorber thickness of 0.7 μm, the 5-stage IC detectors show very low dark current (1.10 × 10−7 A/cm2 at −5 mV and 150 K). Even with un-optimized fabrication and standard commercial (mis-matched) read-out circuit technology, infrared images are obtained by the 320 × 256 IC focal plane array up to 180 K with f/2.3 optics. The minimum noise equivalent temperature difference of 28 mK is obtained at 120 K. These initial results indicate great potential of IC photodetectors, particularly for high operating temperature applications.

Data compressive paradigm for multispectral sensing using tunable DWELL mid-infrared detectors

While quantum dots-in-a-well (DWELL) infrared photodetectors have the feature that their spectral responses can be shifted continuously by varying the applied bias, the width of the spectral response at any applied bias is not sufficiently narrow for use in multispectral sensing without the aid of spectral filters. To achieve higher spectral resolutions without using physical spectral filters, algorithms have been developed for post-processing the DWELLs bias-dependent photocurrents resulting from probing an object of interest repeatedly over a wide range of applied biases. At the heart of these algorithms is the ability to approximate an arbitrary spectral filter, which we desire the DWELL-algorithm combination to mimic, by forming a weighted superposition of the DWELLs non-orthogonal spectral responses over a range of applied biases. However, these algorithms assume availability of abundant DWELL data over a large number of applied biases (>30), leading to large overall acquisition times in proportion with the number of biases. This paper reports a new multispectral sensing algorithm to substantially compress the number of necessary bias values subject to a prescribed performance level across multiple sensing applications. The algorithm identifies a minimal set of biases to be used in sensing only the relevant spectral information for remote-sensing applications of interest. Experimental results on target spectrometry and classification demonstrate a reduction in the number of required biases by a factor of 7 (e.g., from 30 to 4). The tradeoff between performance and bias compression is thoroughly investigated.

Barrier Selection Rules for Quantum Dots-in-a-Well Infrared Photodetector

We report on a systematic study of the effect of barriers on quantum dots-in-a-well infrared photodetectors. Four devices are fabricated and characterized with varying composition for barriers adjacent to quantum dots and away from quantum dots. Effects of these “proximity” and “remote” barriers are studied by comparing photoluminescence, responsivity, dark current, background-limited operating temperature, activation energy, and detectivity. The growth mechanism for a conformal coverage of quantum dots with proximity barriers is described and supported with reflection high-energy electron diffraction and transmission electron microscopy images. It is shown that proximity barriers and remote barriers influence the characteristics of the detector very differently, with increases in proximity barrier energy leading to higher responsivity and lower dark current, while remote barriers reduce the responsivity and dark currents simultaneously. It is demonstrated that confinement enhancing barriers as proximity barriers optimize the SNR at low bias range, suitable for focal plane array applications.

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector.

Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging

Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method.

Embedded Multimodal Registration of Visible Images on Long-Wave Infrared Video in Real Time

We describe an embedded system architecture that implements a real-time multimodal registration method which enables multicamera spatio-temporal feature extraction from the combination of visible and long-wave infrared image sequence. Image registration is performed by matching common features between each frame of a visible image to each frame of an infrared image sequence, in order to estimate an affine transformation between each pair of images. The parameters of this affine transformation are estimated recursively on line with the video, thus enabling image registration in real time. The registration algorithm is implemented using a combination of embedded software and dedicated hardware units on a heterogeneous reconfigurable system-on-a-chip. The hardware performs feature detection and extraction, whereas the software estimates the transformation parameters and maps each infrared video frame onto the visible image coordinates. Our prototype implementation runs with a 135MHz clock, consumes 1.8W and utilizes 29% and 54% of the configurable logic and hardware multiplier units available on the chip, respectively.

A novel readout circuit for on-sensor multispectral classification

A new readout integrated circuit (ROIC) for multispectral classification is presented. The ROIC is designed to utilize the spectral response tunability of dot-in-a-well (DWELL) infrared photodetector to exploit the possibility of real-time on-chip multispectral imaging for classification in analog domain. The unit cells are designed to include all necessary elements needed for spectral classification, including high-voltage time-varying positive and negative biases, bipolar integration, and selective sample-and-hold circuits. A test chip was designed and fabricated using TSMCs 0.35 μm high-voltage technology. The test chip has successfully completed its initial functional tests and is ready for hybridization to a DWELL focal-plane array.

Mid-wave infrared interband cascade photodetectors and focal plane arrays

In recent years, type-II InAs/GaSb superlattices (T2-SLs) have demonstrated dramatic advances and are a serious contender for the high performance infrared (IR) imaging market. The improved understanding of the material properties, as well as the implantation of advanced device architectures, has substantially improved the device performance. Here we will report our efforts to develop mid-IR type-II T2-SL photodetectors and focal plane arrays based on interband cascade structure. The interband cascade photodetector exploits the energy band alignment in the nearly lattice-matched “6.1-Å-family” (InAs, GaSb, AlSb, and their alloys) material system. The InAs/GaSb T2-SL is adopted as the absorber, and two unipolar barriers are placed at each side of the absorber.

Nonuniformity Correction Algorithm Based on a Noise-Cancellation System for Infrared Focal-Plane Arrays ∗

In this paper a novel nonuniformity correction method that compensates for the fixed-pattern noise (FPN) in infrared focal-plane array (IRFPA) sensors is developed. The proposed NUC method compensates for the additive component of the FPN statistically processing the read-out signal using a noise-cancellation system. The main assumption of the method is that a source of noise correlated to the additive noise of the IRFPA is available to the system. Under this assumption, a finite impulse response (FIR) filter is designed to synthesize an estimate of the additive noise. Moreover, exploiting the fact that the assumed source of noise is constant in time, we derive a simple expression to calculate the estimate of the additive noise. Finally, the estimate is subtracted to the raw IR imagery to obtain the corrected version of the images. The performance of the proposed system and its ability to compensate for the FPN are tested with infrared images corrupted by both real and simulated nonuniformity.