About

David J. Brady is a professor at Duke University associated with the Pratt School of Engineering. The page does not provide specific details about his research focus, background, or key contributions, and no biographical information is available beyond his title and affiliation.

Research topics

• Computer Science
• Optics
• Artificial Intelligence
• Physics
• Human–computer interaction
• Nanotechnology
• Algorithm
• Materials science
• Computer vision
• Cartography
• Operating system
• Telecommunications
• Geography

Selected publications

• Radiometric sensitivity and resolution of synthetic tracking imaging for orbital debris monitoring

  Optics Express · 2026-01-07

  articleOpen accessSenior author

  We consider sampling and detection strategies for solar-illuminated space debris. We argue that the lowest detectable debris cross-sectional area (corresponding to a factor of 3-10x in diameter) may be reduced by 10-100x by analysis of stacks of image frames collected at high rates rather than single frame data. In particular, instead of a pixel as a spatial region, the analysis is based on a "phase-space-pixel" which corresponds to an angular velocity and space region and whose intensity is computed by a weighted stacking of spatial pixels corresponding to a test debris trajectory within a wide camera field-of-view (FOV). To isolate debris signals from background, the exposure time is set to match the time it takes for debris to transit through the instantaneous field of view. Debris signatures are detected by multiscale X-ray processing of the data cube. Radiometric analysis of line integrals shows that sub-cm objects in Low Earth Orbit can be detected and assigned full orbital parameters by this approach.

  PublisherDOI

• Space-time imaging

  Handbook of statistics · 2026-01-01

  book-chapterSenior authorCorresponding
  PublisherDOI

• Polarization unlocks scene-level 3D imaging

  Opto-Electronic Advances · 2026-01-01

  articleOpen access1st authorCorresponding

  Scene-level high-precision 3D imaging remians a key challenge due to the trade-offs between imaging distance and accuracy. While polarization-based 3D imaging offers potential, it offen fails facing "discontinuous" targets—environments where multiple objects are separated by space resulting from surface normal integration. A recent work integrating binocular stereo vision with polarization information successfully recovers depth by iteration. This intergation-free appoach paves the way for scene-level high-precision 3D imaging and future applications.

  PublisherOA PDFDOI

• Low latency streaming from a multicamera array to a UHD display wall

  2026-03-05

  articleSenior author
  PublisherDOI

• Integrated metaoptics for super-resolved imaging

  2026-03-05

  article1st authorCorresponding

  The focal field in incoherent imaging systems is undersampled by conventional focal planes. The field contains multidimensional coherence, spectral, polarization, and focal information that cannot be captured by uniform irradiance sampling. Metaoptics integrated on the focal plane offer a path to sampling diversity to overcome this mismatch. Here we describe modal and coherence models of the focal field; we discuss how metaoptics in the near field of the focal plane enable novel sampling, and we describe sampling strategies and sampling metrics. We consider in particular how modal diversity can improve sensor information capacity, MTF, and resolution. Finally, we consider how these strategies impact the roadmap to gigapixel and terapixel cameras.

  PublisherDOI

• Ray Imaging

  SPIE eBooks · 2025-11-21

  book-chapter1st authorCorresponding

  Chapter 4 explores ray-based imaging systems, beginning with geometric optics and progressing through pinhole and coded aperture imaging, projection tomography, and focal systems. It introduces visibility models for ray propagation and discusses how pinhole cameras form images without computation, though with limited resolution and sensitivity. Coded apertures improve throughput and enable 3D imaging by projecting structured shadows onto detectors, allowing for computational reconstruction using linear and neural methods. The chapter then delves into projection tomography, using Radon transforms and convolution backprojection to reconstruct object densities from ray attenuation data. It highlights the advantages of compressive sampling and neural inference in reducing exposure and improving image quality. Coded aperture tomography is extended to spectral and temporal domains, demonstrating snapshot compressive imaging (SCI) for high-dimensional data. Finally, focal imaging is introduced, detailing lens behavior, aberrations, and depth of field. The chapter emphasizes that optimal imaging design depends on signal characteristics, measurement constraints, and computational strategies.

  PublisherDOI

• Sampling Strategy

  SPIE eBooks · 2025-11-21

  book-chapter1st authorCorresponding

  Chapter 10 explores strategies for sampling the multidimensional optical data cube, which includes spatial, spectral, temporal, polarization, and coherence information. It emphasizes the challenge of mapping high-dimensional object data onto 2D focal plane arrays and introduces the concept of separating object, field, and measurement data cubes. The chapter outlines three primary sampling strategies: interleaved (e.g., Bayer filters), temporal scanning (e.g., tunable filters), and parallel sampling (e.g., camera arrays). Feature-specific measurement is discussed using principal component analysis (PCA) to optimize sampling based on object statistics. Spectral and temporal imaging are analyzed through examples like AVIRIS and bead datasets, showing how multiscale sampling can improve efficiency. The chapter also covers dynamic range management via multiple exposures and tone mapping, autofocus strategies using reinforcement learning, and lens design in the context of microcamera arrays. It concludes with advanced topics like interferometric focal planes and compressive video encoding, highlighting the need for efficient analog-to-digital conversion and low-power image signal processing to enable high-throughput computational imaging.

  PublisherDOI

• Divide and conquer: parallel processing in computational imaging

  Advanced Photonics · 2025-09-29

  articleOpen access1st authorCorresponding
  PublisherDOI

• Image Estimation

  SPIE eBooks · 2025-11-21

  book-chapter1st authorCorresponding
  PublisherDOI

• Computational Optical Imaging

  SPIE eBooks · 2025-11-21

  book1st authorCorresponding

  SPIE Press is the largest independent publisher of optics and photonics books - access our growing scientific eBook collection ranging from monographs, reference works, field guides, and tutorial texts.

  PublisherDOI

Recent grants

• Collaborative Research: IDBR: New Technology to Measure Oganismal Color Phenotypes: 3D, Hyperspectral, UV-Vis, Virtual Reality Models of Organismal Coloration

  NSF · $111k · 2009–2012

Frequent coauthors

• Zhan Ma

  Nanjing University

  89 shared

• Weipeng Zhao

  Zhongshan Hospital

  82 shared

• Yi Lin

  Sun Yat-sen University

  82 shared

• Lili Dong

  Fudan University

  82 shared

• Daniel L. Marks

  80 shared

• Xun Cao

  79 shared

• Yi Si

  Zhejiang University

  78 shared

• You Zhou

  Zhongshan Hospital

  77 shared

Labs

• David J. Brady LabPI