Scene-Based Non-Uniformity Correction (SBNUC) algorithm to lower the polarimetric noise

OUSD (R&E) MODERNIZATION PRIORITY: Microelectronics TECHNOLOGY AREA(S): Electronics The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop a long wave infrared (LWIR), scene-based, non-uniformity correction (NUC) algorithm to lower the noise in polarimetric micro-grid sensors suitable for use on small unmanned aerial vehicles (UAV). DESCRIPTION: Fixed pattern noise in infrared focal plane arrays affects the ability to detect, recognize and identify targets. Two-point correction is often used make offset and gain corrections to the image to lower the fixed pattern noise. However, factory correction is inadequate due to the drift of pixel response over time. Correction in the field is often cumbersome, impractical, and inadequate to accommodate changing scene conditions. The problem becomes worse when dealing with polarization because of the image processing involved in generating the polarization images. For example, microgrid sensors typically have four polarization filters, horizontal(H), vertical(V), and two filters at 45 degrees and 135 degrees. Calculating the Stokes vector involves adding, subtracting, and dividing. Calculating the Degree of Linear Polarization (DoLP) involves squaring square rooting, and dividing. Each of these operations increases the noise. The Army requires a correction for pixel drift and changing scenes using microgrid, microbolometer sensors operating from a moving platform such as a small UAV. The algorithm(s) should operate using sensors at a 30 hertz frame rate and an F/1 lens. PHASE I: Phase I consists of the development or adaptation of an SBNUC process using image sequence or video data that has characteristics of imagery collected from a small UAS using a minimum number of images or scene changes. It is required that a quantitative improvement in performance of the SBNUC process over that of a conventional two-point NUC be established. Analysis shall include a comparison of mean and standard deviation of degree of linear polarization (DoLP) noise characteristics in addition to other image comparison algorithms or metrics. Shortcomings of the developed process should be described. The path to make the approach more robust under a greater variety of scene and platform motion conditions to be implemented in Phase II should be described. It is necessary to eliminate to the greatest extent possible any dependence on additional hardware or specific motions of the platform or a gimbal to achieve scene-based corrections. Trade- offs that may be necessary to achieve the SBNUC improvement shall be identified. PHASE II: Phase II consists of the implementation, testing, and optimization of the polarimetric SBNUC process on real data collected from a small UAS platform. Further, data shall be collected and the SBNUC shall be demonstrated under a variety of environmental, background, and time of day conditions. The improvement shall be demonstrated using the analysis developed in the Phase I. Any potential sensor or other hardware improvements for optimization shall be identified. PHASE III DUAL USE APPLICATIONS: The commercialization of this process is expected to provide low cost, high performance uncooled cameras that operate over a wide range of conditions. Potential uses are in a variety of military applications including sensors for manned and unmanned aerial and ground platforms for clutter suppression, target detection and tracking, and in commercial applications including environmental monitoring, security/law enforcement, border patrol, and homeland security. REFERENCES: B.M. Ratliff, et al, Adaptive Scene-based Correction Algorithm for Removal of Residual Fixed Pattern Noise in Microgrid Image Data , Proc. SPIE 8364, 11 June 2012. R.C. Hardie, et al., Super-resolution for imagery from integrated microgrid polarimeters, Opt. Express 19, 12937 12960 (2011). J.E. Hubbs, et al, Measurement of the radiometric and polarization characteristics of a microgrid polarizer infrared focal plane array, Proc. SPIE, Infrared Detectors and Focal Plane Arrays VIII, 6295, 62950C (2006). Jun-Hyung Kim, et al, Regularization approach to scene-based non uniformity correction, Optical Engineering, 53(5), 053105 (2014). KEYWORDS: Scene-Based Non-Uniformity Correction; Polarization; Infrared Focal Plane Array