Measurement of the Modulation Transfer Function of Infrared Imaging System by Modified Slant Edge Method

Li, Hang; C, Yan; Jianbing, Shao

doi:10.3807/josk.2016.20.3.381

Cited by 22 publications

(12 citation statements)

References 11 publications

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“…Further improvements can be made by adjusting the standard algorithm, but at the cost of moving away from the ISO method. For example, adjusting the ESF resampling process to better conform to the projection down the edge slope was shown to improve the e-SFR measure in multiple studies [29,30,40,41]. These studies provided results with reduced bias introduced by image noise, edge angle variations and ROI size variations.…”

Section: Discussionmentioning

confidence: 99%

Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data

Zwanenberg¹,

Triantaphillidou²,

Jenkin³

et al. 2021

jist

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The Natural Scene derived Spatial Frequency Response (NS-SFR) is a novel camera system performance measure that derives SFRs directly from images of natural scenes and processes them using ISO12233 edge-based SFR (e-SFR) algorithm. NS-SFR is a function of both camera system performance and scene content. It is measured directly from captured scenes, thus eliminating the use of test charts and strict laboratory conditions. The effective system e-SFR can be subsequently estimated from NS-SFRs using statistical analysis and a diverse dataset of scenes. This paper first presents the NS-SFR measuring framework, which locates, isolates, and verifies suitable step-edges from captures of natural scenes. It then details a process for identifying the most likely NS-SFRs for deriving the camera system e-SFR. The resulting estimates are comparable to standard e-SFRs derived from test chart inputs, making the proposed method a viable alternative to the ISO technique, with potential for real-time camera system performance measurements.

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Section: Discussionmentioning

confidence: 99%

Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data

Zwanenberg¹,

Triantaphillidou²,

Jenkin³

et al. 2021

jist

View full text Add to dashboard Cite

show abstract

“…Since the pixel sizes for the two sensors did not match, the number of pixels in the ROI was selected so that the data from both sensors covered the same physical dimensions on the object. The angle of the slanted edge was calculated from the ROI using a combination of gradient [4], edge detection, and line fitting. This process accounted for any misalignment between the imaging sensor and the object in the experimental setup.…”

Section: Discussionmentioning

confidence: 99%

“…This step is important because a difference of less than one degree can have adverse effects on the outcome of the MTF calculation. After the angle was determined, the pixels in the ROI were projected onto a new coordinate system, rotated so that the y-axis was parallel to the edge [4]. This projection (figure 6) is how the up-sampling of the ESF occurs, creating a super-sampled ESF without interpolation.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the performance of the bistatic Frame-based Laser Imager (FLI)

Alley

Cooper

2023

Ocean Sensing and Monitoring XV

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Optical sensors play a significant role in maritime sensing. The high spatial resolution of cameras is vital to object identification, but the performance of flood illuminated CCD/CMOS cameras quickly deteriorates in degraded visual environments (DVE) such as turbid coastal waters and murky harbors. Several laser-based sensors have been developed over the past few decades to enhance optical imaging in DVEs. However, since conventional approaches require the laser and receiver be located on the same platform, the size, weight, and power (SWaP) requirements are incompatible with small remotely operated vehicles (ROVs).Researchers at the Naval Air Warfare Center -Aircraft Division in Patuxent River, MD (NAWCAD PAX) developed [6] and patented [8] an optical imager utilizing a bistatic geometry where the laser and receiver are mounted on separate, smaller platforms. This allows the SWaP of the sensor components to be optimized for small platforms, like ROVs. The transmitter uses a modulated laser beam and a microelectromechanical system (MEMS) scanner to sequentially illuminate an underwater object in a raster fashion. A distant receiver collects the object reflected laser light and reconstructs the imagery. The transmitter scans the object at frame rates comparable to video cameras, producing a laser video stream. This paper will report on laboratory tests comparing the spatial resolution performance, as a function of attenuation length, of the bistatic frame-based laser imager (FLI) to a conventional flood illuminated camera typical of small ROVs.

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“…Detector board Americium source A standard approach to determine a detectors resolution is the slanted edge method [16], where part of the detector is covered with an object at a lower angle [17,15]. As an edge, we used a 1 mm thick Palladium plate placed about 1 cm above the detector surface and slanted by ca.…”

Section: Readout System Detectormentioning

confidence: 99%

A USB 3.0 readout system for Timepix3 detectors with on-board processing capabilities

et al. 2018

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Timepix3 is a high-speed hybrid pixel detector consisting of a 256x256 pixel matrix with a maximum data rate of up to 5.12 Gbps (80 MHit/s). The ASIC is equipped with eight data channels that are data driven and zero suppressed making it suitable for particle tracking and spectral imaging.In this paper, we present a USB 3.0-based programmable readout system with online preprocessing capabilities. USB 3.0 is present on all modern computers and can, under real-world conditions, achieve around 320 MB/s, which allows up to 40 MHit/s of raw pixel data. With on-line processing, the proposed readout system is capable of achieving higher transfer rate (approaching Timepix4) since only relevant information rather than raw data will be transmitted.The system is based on an Opal Kelly development board with a Spartan 6 FPGA providing a USB 3.0 interface between FPGA and PC via an FX3 chip. It connects to a CERN T 3 chipboard with standard VHDCI connector via a custom designed mezzanine card. The firmware is structured into blocks such as detector interface, USB interface and system control and an interface for data pre-processing. On the PC side, a Qt/C++ multi-platform software library is implemented to control the readout system, providing access to detector functions and handling high-speed USB 3.0 streaming of data from the detector.We demonstrate equalisation, calibration and data acquisition using a Cadmium Telluride sensor and optimise imaging data using simultaneous ToT (Time-over-Threshold) and ToA (Timeof-Arrival) information. The presented readout system is capable of other on-line processing such as analysis and classification of nuclear particles with current or larger FPGAs.

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Measurement of the Modulation Transfer Function of Infrared Imaging System by Modified Slant Edge Method

Cited by 22 publications

References 11 publications

Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data

Estimation of ISO12233 Edge Spatial Frequency Response from Natural Scene Derived Step-Edge Data

Quantifying the performance of the bistatic Frame-based Laser Imager (FLI)

A USB 3.0 readout system for Timepix3 detectors with on-board processing capabilities

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