Empirical frequency domain model for fixed-pattern noise in infrared focal plane arrays

Pérez, Francisco; Pezoa, Jorge E.; Figueroa, Miguel; Torres, Sergio N.

doi:10.1016/j.infrared.2014.09.010

Cited by 13 publications

(9 citation statements)

References 23 publications

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“…The image of Fig. 2a was FPN corrected by using the two-point calibration method (TPC) 13 – 15 , that utilizes a first-order model for the camera response. This calibration technique was applied using two uniform radiation sources acquired from two high-accuracy blackbody radiators in the Mikron M300 series, at 20 °C and 30 °C, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, blackbody samples were recorded by the Amber camera at temperatures of 20 °C and 30 °C, by the Lumasense camera at 100 °C and 350 °C, and by the QDIP camera at 26 °C and 50 °C. By employing these data and a TPC version of the L f , the noise pattern of these technologies is artificially superimposed on the clean L f using a first-order approximation for the response of the camera 13 – 15 , Subsequently, the L f with FPN is generated, and the proposed method is applied (see Fig. 6 ).…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, the second architecture considers a different camera of the same technology for each image of the L f (solid line). This design is simulated by using the procedure 15 for synthesizing different FPN samples that preserve the technology-dependent spatial structure of the real FPN of a camera. As shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Infrared light field imaging system free of fixed-pattern noise

Coelho

Tapia

Pérez

et al. 2017

Sci Rep

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Digital photonic sensors have greatly evolved to maximize sensitivity and spatial, spectral, and temporal imaging resolution. For low-energy photons, new designs have generated new types of noise that degrade the formed-image signal-to-noise ratio to values lower than 1. Fixed-pattern noise (FPN), which is produced by the non-uniform focal-plane-array optoelectronics response, is an ill-posed problem in infrared and hyperspectral imaging science. Here, we experimentally show that the FPN behaves as an object at a depth of infinity when a light field is captured by an imaging system. The proposed method is based on the capture of the light field of a scene and digital refocusing to any nearby objects in the scene. Unlike standard techniques for FPN reduction, our method does not require knowledge of the physical parameters of the optoelectronic transducer, the motion scene, or the presence of off-line blackbody sources. The ability of the proposed method to reduce FPN is measured by evaluating the structural similarity (SSIM) index employing a blackbody-based FPN reduction technique as a reference. This new interpretation of the FPN opens avenues to create new cameras for low-energy photons with the ability to perform denoising by digital refocusing.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Infrared light field imaging system free of fixed-pattern noise

Coelho

Tapia

Pérez

et al. 2017

Sci Rep

Self Cite

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“…Although many studies investigate the presence of noise in IR images: in order to reduce it [1,2,3] or to assess its effect on quality [4], very few investigate the effect of compression on IR images.…”

Section: Introductionmentioning

confidence: 99%

Compression of Infrared images

Mantel¹,

Forchhammer²

2017

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This paper investigates the compression of infrared images with three codecs: JPEG2000, JPEG-XT and HEVC. Results are evaluated in terms of SNR, Mean Relative Squared Error (MRSE) and the HDR-VDP2 quality metric. JPEG2000 and HEVC perform fairy similar and better than JPEG-XT. JPEG2000 performs best for bits-per-pixel rates below 1.4 bpp, while HEVC obtains best performance in the range 1.4 to 6.5 bpp. The compression performance is also evaluated based on maximum errors. These results also show that HEVC can achieve a precision of 1 • C with an average of 1.3 bpp.

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“…This led Pezoa and Medina to model the Non-Uniformity (NU) noise common in LWIR images in the frequency domain [16] as distinct from independent spatial noise. Using the NU model in place, Pérez et al measured and compared the efficacy of several Non-Uniformity correction (NUC) algorithms [17] and developed methods for extracting the structure of the underlying fixed-pattern noise (FPN) [18].…”

Section: Chapter 1: Introductionmentioning

confidence: 99%

Tasking on Natural Statistics of Infrared Images

Goodall

Bovik

Paulter

2016

IEEE Trans. on Image Process.

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Humans are the most important factor for assessing distortion and quality in IR images, which are often used in perception tasks. Therefore, predicting human performance when a task involving LWIR images needs to be performed can be critical to improving task efficacy. The National Institute for Standards and Technology (NIST) characterizes human Targeting Task Performance (TTP) by asking firefighters to identify the locations of fire hazards in LWIR images under distorted conditions. We find that vi task performance can be predicted using NSS features. We also report the results of a human study.We analyzed the NSS of LWIR images under pristine and distorted conditions using four databases of LWIR images. Each database was captured with a different camera allowing us to better evaluate the statistics of LWIR images independent of camera model. We find that models of NSS are also effective for measuring distortions in the presence of other independent distortions. vii

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Empirical frequency domain model for fixed-pattern noise in infrared focal plane arrays

Cited by 13 publications

References 23 publications

Infrared light field imaging system free of fixed-pattern noise

Infrared light field imaging system free of fixed-pattern noise

Compression of Infrared images

Tasking on Natural Statistics of Infrared Images

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