X-ray energy self-adaption high dynamic range (HDR) imaging based on linear constraints with variable energy

Li, Yihong; Han, Yan; Chen, Ping

doi:10.1109/jphot.2017.2778719

Cited by 7 publications

(14 citation statements)

References 17 publications

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“…When using multi-exposure X-ray imaging, one still needs to calibrate the source energy for each exposure. To circumvent this calibration, Li et al discuss an automated approach in [29]. Beyond the case of a single projection angle, recently, Chen et al employed exposure adaption between different scanning angles in [17].…”

Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

“…Clearly, the approaches reviewed so far [16,25,29,17] take X-ray imaging into a new, largely unexplored direction. That said, since the HDR X-ray imaging is based on multiple exposure HDR photography, the former also inherits the limitations of HDR photography.…”

Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

“…(b) Exposure calibration. HDR X-rays rely on multiple exposures that should be calibrated [29,17] so that the eventual reconstruction indeed reveals HDR features. (c) Tone mapping and sensor response.…”

Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

“…The unconventional aspect of the MRT is that instead of encoding pointwise Radon projections that may potentially saturate, the MRT incorporates a modulo operation that performs a reset before the saturation level is reached (see Figure 1 for the results of a hardware demonstration). This allows to locally capture changes of the signal values, hence circumventing sensor saturation or clipping as they appear in the multi-exposure methods analyzed in [16,44,29]. At the same time, the number of resets performed is not part of the acquired data, so one encounters a different information loss.…”

Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

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The Modulo Radon Transform: Theory, Algorithms and Applications

Beckmann¹,

Bhandari²,

Krahmer³

2021

Preprint

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Recently, experiments have been reported where researchers were able to perform high dynamic range (HDR) tomography in a heuristic fashion, by fusing multiple tomographic projections. This approach to HDR tomography has been inspired by HDR photography and inherits the same disadvantages. Taking a computational imaging approach to the HDR tomography problem, we here suggest a new model based on the Modulo Radon Transform (MRT), which we rigorously introduce and analyze. By harnessing a joint design between hardware and algorithms, we present a singleshot HDR tomography approach, which to our knowledge, is the only approach that is backed by mathematical guarantees. On the hardware front, instead of recording the Radon Transform projections that my potentially saturate, we propose to measure modulo values of the same. This ensures that the HDR measurements are folded into a lower dynamic range. On the algorithmic front, our recovery algorithms reconstruct the HDR images from folded measurements. Beyond mathematical aspects such as injectivity and inversion of the MRT for different scenarios including band-limited and approximately compactly supported images, we also provide a first proof-of-concept demonstration. To do so, we implement MRT by experimentally folding tomographic measurements available as an open source data set using our custom designed modulo hardware. Our reconstruction clearly shows the advantages of our approach for experimental data. In this way, our MRT based solution paves a path for HDR acquisition in a number of related imaging problems.

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Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

Section: Towards High-dynamic-range Tomographymentioning

confidence: 99%

See 2 more Smart Citations

The Modulo Radon Transform: Theory, Algorithms and Applications

Beckmann¹,

Bhandari²,

Krahmer³

2021

Preprint

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“…When conventional Radon projections exceed the dynamic range of the detector, the resulting measurements are saturated or clipped, leading to a permanent loss of information. To overcome this bottleneck, in the recent years, independent groups of researchers have explored the idea of HDR tomography [3][4][5][6] which is largely inspired by the multi-exposure fusion approach in computational photography [7,8]. By fusing X-ray exposures, for example, at different tube voltages [3], HDR reconstruction has been experimentally verified.…”

Section: Introductionmentioning

confidence: 99%

MR. TOMP : Inversion of the Modulo Radon Transform (MRT) via Orthogonal Matching Pursuit (OMP)

Beckmann

Bhandari

2022

2022 IEEE International Conference on Image Processing (ICIP)

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In the recent years, practitioners in the area of tomography have proposed high dynamic range (HDR) solutions that are inspired by the multi-exposure fusion strategy in computational photography. To this end, multiple Radon Transform projections are acquired at different exposures that are algorithmically fused to facilitate HDR reconstruction. A single-shot alternative to multi-exposure fusion approach has been proposed in our recent line of work which is based on the Modulo Radon Transform (MRT). In this case, Radon Transform projections are folded via modulo non-linearity. This folding allows HDR values to be mapped into the dynamic range of the sensor and, thus, avoids saturation or clipping. The folded measurements are then mapped back to their ambient range using algorithms. The main goal of this paper is to introduce a novel, Fourier domain recovery method, namely, the OMP-FBP method, which is based on the Orthogonal Matching Pursuit (OMP) algorithm and Filtered Back Projection (FBP) formula. The proposed OMP-FBP method offers several advantages; it is agnostic to the modulo threshold or the number of folds, can handle much lower sampling rates than previous approaches and is empirically stable to noise and outliers. Computer simulations as well as hardware experiments in the paper validate the effectivity of the OMP-FBP recovery method.

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