Single-shot HDR using conventional image sensor shutter functions and optical randomization

Kristina Monakhova; Kyrollos Yanny; Nicholas Antipa; Xiang Dai

REVIEW 3 major objections 5 minor 3 references

Single-shot HDR using conventional image sensor shutter functions and optical randomization

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read An off-the-shelf sensor and a random fiber bundle can capture a 73 dB HDR image in a single 8-bit frame.

desk verdict A clever, well-supported single-shot HDR architecture with real hardware evidence; the main unvalidated assumption is the GRR shutter timing model, which should be checked before trusting the 73 dB claim. read the letter →

arxiv 2506.22426 v2 pith:MJ3UVQZE submitted 2025-06-27 eess.IV cs.CVcs.GReess.SPphysics.optics

Xiang Dai , Kyrollos Yanny , Kristina Monakhova , Nicholas Antipa This is my paper · ORCID

classification eess.IVcs.CVcs.GReess.SPphysics.optics

keywords highdynamicrangeimagingsingle-shotHDRglobalresetreleaseshutterrandomfiberbundleopticalrandomizationcomputationaltotalvariationregularizationspatiallyvaryingexposure

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes a single-shot HDR imaging architecture built entirely from off-the-shelf parts: a conventional sensor run in global reset release (GRR) shutter mode, plus an optical element that randomly permutes the image before it lands on the sensor. Because GRR mode gives each row a slightly different exposure time, the permutation turns the smooth row-wise exposure gradient into a spatially random exposure map across the scene. The authors show that this randomization makes the HDR recovery problem well posed under a simple total variation prior, and they validate the idea in simulation and with a physical prototype using a random fiber bundle. Their central quantitative claim is that the prototype reconstructs a dynamic range of up to 73 dB from a single 8-bit measurement whose sensor is specified at 48 dB. If correct, this means a camera built from commodity components could capture scene contrast of roughly 5000:1 in one frame, with no multi-exposure bracketing and no per-scene tuning.

What carries the argument

The load-bearing object is the permutation matrix $P$ and the diagonalization identity $\Lambda = P^T S P$. Here $S$ is the diagonal exposure matrix of the GRR shutter, whose entry for row $u$ is $S[u,v] = T_0 + t_r(u-1)$ — the initial exposure plus the line-by-line readout time — and $P$ is an idealized optical randomizer that maps each scene point to a pseudorandom sensor pixel. The identity shows that after de-shuffling, the measurement equals a clipped, randomly re-exposed version of the scene, so the GRR gradient is unfolded from a smooth ramp into pseudorandom pixel-wise exposures. The recovery then treats saturated pixels as erasures via a diagonal mask $E$ and solves a TV-regularized least-squares problem, so the machinery's work is to turn an ill-posed inpainting problem over large clipped regions into a well-posed inverse problem over scattered missing pixels.

What would settle it

Repeat the ND-filter quantitative validation with the prototype, then re-measure the system matrix $P$ from scratch and compare the two reconstructions of the same static scene; if the second reconstruction loses more than a few dB of dynamic range or shows visible model-mismatch artifacts, the claim that the calibrated $P$ faithfully supports the 73 dB result is falsified.

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Extended reading notes

Core claim

The paper's central discovery is that pairing a random permutation of the scene with the GRR shutter's linear exposure gradient is mathematically equivalent to applying a spatially random exposure to the scene before quantization. Concretely, if $P$ is a permutation matrix, the de-shuffled measurement takes the form $\hat{\mathbf{b}} = Q\{\Lambda \mathbf{x}\}$ with $\Lambda = P^T S P$ a diagonal matrix whose entries are the GRR exposure times randomly reordered; the proof is that $P^T$ commutes with the pointwise nonlinearity $Q$ and permutes the diagonal of $S$. The consequence is that every local patch of the scene contains a mixture of long and short effective exposures, so saturated pixels are scattered rather than contiguous and can be inpainted from well-exposed neighbours using only total variation. The paper demonstrates this both in simulation, where it outperforms prior single-shot methods at 1% and 10% saturation, and in hardware, where a random fiber bundle coupled to a commercial sensor recovers an ND-filter test scene spanning a measured attenuation ratio of about 5000:1, or 73 dB.

Load-bearing premise

The whole scheme stands on the premise that the optical system is accurately captured by a known, near-permutation matrix $P$ and that the GRR shutter timing follows the ideal row-wise model $S[u,v] = T_0 + t_r(u-1)$; if the real fiber bundle's mapping drifts after calibration or the shutter timing deviates, the unshuffled measurement and the claimed 73 dB result would not hold.

Editorial extensions

If this is right

Single-shot HDR can be achieved with stock sensors and ordinary optics, eliminating the need for custom dual-exposure pixels or costly sensor redesigns.
Large, contiguous highlight regions such as bright sky or lamps are recoverable because the random exposure map scatters saturation into small clusters that a local prior can inpaint.
The operating dynamic range is tunable through two sensor parameters, the base exposure $T_0$ and the row readout time $t_r$, so the same hardware can be reconfigured for different scene brightness ranges.
A real prototype with an off-the-shelf random fiber bundle achieves about 73 dB from an 8-bit measurement, approximately 25 dB beyond the sensor's native range.
Because the forward model is linear and the prior is simple TV, the method needs no per-scene training or exposure optimization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same principle could be ported to video or endoscopy: any sensor with a row-wise exposure gradient, combined with a fixed random mapping, yields per-pixel exposure diversity in a single frame, at the cost of motion blur sensitivity.
The calibration bottleneck (about 0.7 seconds per column of $P$) suggests that moving from a fiber bundle to a bonded, near-permutation mapping, or to learned blind reconstruction, is the natural path to practicality.
If the ideal-permutation analysis carries over to real bundles, the method's saturation-robustness argument predicts that performance should improve as the fiber mapping approaches a true permutation; a direct test would compare reconstruction quality against measured clumping or crosstalk statistics.
The patch-wise dynamic range analysis implies a testable prediction: scenes with isolated highlights (for example, sunlight through leaves) should reconstruct worse than scenes with clustered highlights, consistent with the correlation the paper reports.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a genuine single-shot HDR architecture that works on a real bench, and the math behind it is simple enough to check in an afternoon. The main thing I'd want before believing the headline 73 dB number is an independent check of the GRR shutter's row-exposure ramp; the paper assumes it from specs.

What's new: pairing the global reset release shutter mode with a pseudorandom spatial permutation. The observation in Eq. (7) — that with a permutation matrix P, P^T S P is just the exposure matrix with entries randomly reordered — is clean and correct. That turns the GRR exposure gradient into a spatially random exposure map, which makes the inpainting problem much better posed than raw GRR or global shutter with TV. The simulations are thorough: 180 SI-HDR scenes, several metrics, ablations showing the combination beats either component alone, and fair comparison to HDR-CNN, Deep Optics HDR, and ND filter arrays. The hardware demonstration is real: a random fiber bundle, a calibrated PSF matrix, and an ND-filter ramp whose reconstructed profile tracks a multi-shot ground truth over about 5000:1. They also show the fiber-to-fiber transmittance variation can't explain the result. That's solid evidence the idea works.

Soft spots: the stress-test note is right that S is never independently measured. The paper states T0=189 us and t_r=51 us and assumes the linear row-wise model Eq. (5). A flat-field GRR capture would have settled whether the ramp is really linear, including reset delays or clock quantization. If the true exposure map is nonlinear, the forward model used in inversion is wrong, and the 73 dB figure is contaminated. The reconstructed profile tracking the ND filter suggests the true S is close to the model, but "close" isn't a measured validation. Second, the quantitative hardware result is a single intensity profile from one scene, no error bars, and the qualitative results look decent but clearly not clean. Those are documented limitations, not hidden flaws. The calibration procedure is heavy (0.7 s per column, 940 MB matrix), so this is a proof-of-concept, not a product. No code or data released either.

Bottom line: this deserves a serious referee. The core idea is correct, the simulation evidence is strong, and the prototype is a genuine existence proof. I'd want the authors to add a flat-field GRR validation and repeated measurements before I'd fully trust the 73 dB, but that's a revision, not a reason to reject. This is the kind of paper I'd cite and bring to reading group.

Referee Report

3 major / 5 minor

Summary. The paper proposes a single-shot HDR imaging architecture that combines the global reset release (GRR) shutter mode of an off-the-shelf CMOS sensor with optical randomization provided by a random fiber bundle. The forward model is b = Q{SPx + eta}, where S is a diagonal row-wise exposure ramp and P is a (calibrated, approximately permuting) optical transport matrix. The authors show analytically that for an ideal permutation P, the de-shuffled measurement is Q{Lambda x} with Lambda a diagonal matrix of randomly reordered exposures, so the system implements spatially random exposure before quantization. HDR recovery is posed as an erasure-regularized inverse problem with total variation. Extensive simulations on the 180-image SI-HDR dataset compare the method against HDR-CNN, Deep Optics HDR, and a 2x2 ND filter array, showing competitive or better performance, especially at high saturation rates. A hardware prototype using a random fiber bundle and an Allied Vision 1800 U-1240 camera is calibrated by measuring the system PSF, and a quantitative ND-filter experiment reports a recovered dynamic range of approximately 73 dB from a single 8-bit measurement. Limitations, including model mismatch, motion blur, and depth-dependent defocus, are discussed in Section 6.

Significance. If the hardware claims hold, this is a practically valuable contribution: it extends the dynamic range of an inexpensive, unmodified sensor using off-the-shelf optics and a lightweight TV prior, avoiding the cost and complexity of custom sensors. The core analytical observation that GRR exposure combined with a permutation matrix yields random exposures is correct and is a clean, parameter-free design insight. The simulation study is unusually thorough, covering six quality metrics, ablations, saturation sweeps, and a 180-scene dataset. The hardware validation is also a strength: the authors calibrate a full system PSF, validate against external ground truth (multi-shot HDR and a 14-bit camera), test two orientations of the ND filter, and provide a control simulation showing that fiber transmittance variation alone cannot explain the result. The main weakness is that the GRR shutter model S in Eq. (5) is assumed from specifications and never directly validated, which directly affects the trustworthiness of the headline 73 dB hardware number.

major comments (3)

[Section 5.3, Eq. (5)] The GRR exposure model S[u,v] = T0 + t_r (u-1) is used to form the forward model A = SP in Eq. (4), but it is never experimentally validated. No flat-field capture in GRR mode is shown to confirm the assumed linear row-wise exposure ramp, the values T0 = 189 us and t_r = 51 us, or the absence of row-timing nonlinearities (e.g., reset delays, clock quantization, dead time). Because the data-fidelity term in Eq. (8) uses this S, a systematic error in the exposure map would bias the recovered radiance and directly contaminate the max/min ratio used to compute the 73 dB figure. I recommend adding a direct calibration of the effective exposure map (e.g., flat-field GRR captures at several controlled light levels, or a comparison of the measured flat-field gradient against Eq. (5)) and/or a sensitivity analysis showing that the reconstructed dynamic range is robust to plausible deviations from linear row timing.
[Section 5.3, Fig. 9(c)] The headline claim of 73 dB dynamic range rests on a single measured intensity profile from a single ND-filter sample. The maximum-to-minimum ratio of approximately 5000 is extracted from one line across the reconstruction, and although Appendix C.2 repeats the measurement with two orientations, this is still a single scene and a single profile. The paper should report the uncertainty in this estimate (e.g., variation across multiple profile positions, repeat captures, or the noise floor of the reconstruction) or present additional quantitative samples. As written, the reader cannot distinguish a robust system-level property from a favorable single realization.
[Section 6.4 and Section 5.1] The paper acknowledges model mismatch from clumping, crosstalk, broken fibers, stray light, and nonuniform transmittance, but it never quantifies how far the calibrated P deviates from a permutation or how sensitive the recovered HDR is to calibration errors. The 73 dB hardware result is obtained by inverting this P via Eq. (8), so the accuracy of P is load-bearing. I recommend reporting calibration residual statistics (e.g., the fraction of energy in the expected fiber location, the condition number or coherence of P, or the correlation between measured and predicted PSFs) and, if feasible, a simulation in which the calibrated P is perturbed by the observed mismatch to bound the effect on reconstructed dynamic range.

minor comments (5)

[Section 5.3] The sentence "we reconstruct images from two different LED lamp orientations to eliminate ensure that we did not achieve high dynamic range" contains a typo ('eliminate ensure' should be 'eliminate the possibility' or 'ensure').
[Section 2] The sentence "Event sensors with learning-based methods can reconstruct high-speed HDR video. [Rebecq et al. 2019; Zou et al. 2021]" is missing connecting text and has a period before the citation; it should be merged into the preceding discussion.
[Section 5.3] The paper uses both "up to 73 dB" (abstract, introduction) and "around 73 dB" (Section 5.3). Please standardize the phrasing so the claim is unambiguous.
[Appendix C.4] The control simulation in Fig. 19 is described only briefly; it would help to state explicitly what metric or visual criterion shows that the fiber transmittance variation 'fails completely' for the test scene.
[Section 6.4] The phrase "approximately <5%" is awkward; consider writing 'approximately 5% or fewer' or 'less than 5%'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the core randomization derivation is a self-contained matrix identity, and the 73 dB hardware claim is validated against independent multi-shot HDR ground truth.

full rationale

The central derivation in Section 3.3 (Eqs. 6-7, with proof in Appendix A) is a self-contained algebraic identity: for a permutation matrix P and diagonal exposure matrix S, Lambda = P^T S P is the diagonal matrix with permuted entries. This does not assume the HDR recovery result it is used to motivate. The recovery problem (Eq. 8) uses an erasure matrix E defined directly from the clipped measurement and a standard TV regularizer; no parameter is fit to produce the 73 dB claim. The hardware validation in Section 5.3 compares the reconstructed ND-filter luminance profile against an independent ground truth from a Canon 5D Mark II multi-shot HDR merge and a 14-bit raw capture, so the reported ~5000:1 contrast ratio is an externally checked measurement rather than a consequence of the forward model. The GRR exposure model S[u,v] = T0 + t_r(u-1) in Eq. (5) is an assumption from sensor specifications and is not separately validated, and Section 6.4 correctly lists model-mismatch sources such as stray light, crosstalk, and broken fibers; these are correctness risks, not circular reductions, because the empirical comparison to external ground truth would fail if the model were substantially wrong. No load-bearing self-citations or author-imported uniqueness theorems appear in the reference list.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. It relies on standard modeling assumptions: static scene, linear clipped camera response, ideal GRR exposure timing, and a known (near-permutation) optical matrix. The tunable parameters are the TV weight tau and the exposure settings T0/tr, which affect reconstruction quality. The random-exposure 'better posed' claim is supported empirically by ablations and a 1D sampling analogy, not by a formal guarantee.

free parameters (3)

TV regularization weight tau = not reported in the paper
The total variation prior weight in Eq. (8). The paper uses tau=0 and tau>0 in ablations but does not give the exact value used for reported numbers, so it is a user-tuned hyperparameter affecting output quality.
Exposure settings T0 and t_r (or ratio T0/t_r) = T0=189 us, t_r=51 us (hardware); simulations tune to cover the exposure bracket
These control the dynamic range and are shown in Appendix Fig. 23 to affect Q-score. In the 1%/10% baseline tests the shutter function is adjusted per scene to reach the target saturation, so the system is not fully fixed-parameter in those comparisons.
Calibration thresholds (correlation > 0.95, minimum 3 valid exposures) = not stated as tunable
Used to build the sparse PSF matrix P; hand-chosen thresholds that affect calibration fidelity.

assumptions (5)

domain assumption Scene and optical system are time-invariant during the exposure
Eq. (1) replaces the time integral with pointwise multiplication; motion causes blur, which the paper acknowledges and analyzes in Sec. 6.2.
domain assumption Camera response is linear within ADC range and clips outside; noise is additive
The Q(.) model in Eq. (3) is a standard idealization; ignores Poisson noise and nonlinear response. The paper uses additive Gaussian noise in simulation with sigma=0.01.
domain assumption GRR shutter exposure time follows S[u,v]=T0+t_r(u-1) exactly
Eq. (5) assumes an ideal linear gradient based on readout timing; real sensor timing may deviate, and the paper does not calibrate the shutter function.
domain assumption The optical mapping P is a permutation matrix (theory) or is known accurately via calibration (practice)
The theoretical random-exposure result (Eq. 7) relies on P being a permutation. The hardware uses a calibrated sparse P, but fiber clumping, crosstalk, and broken fibers make it non-ideal; the paper's Sec. 6.4 states reconstruction depends on precise knowledge of the transfer function.
standard math Total variation is a suitable prior for natural HDR scenes
Standard image prior; the paper uses it to inpaint saturated pixels.

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Cite this review

Pith. "Pith review of Single-shot HDR using conventional image sensor shutter functions and optical randomization." pith.science (2026). https://pith.science/paper/MJ3UVQZE

@misc{pith2026250622426,
  author       = {Pith},
  title        = {Pith review of: Single-shot HDR using conventional image sensor shutter functions and optical randomization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MJ3UVQZE}},
  note         = {Machine review of arXiv:2506.22426}
}

read the original abstract

High-dynamic-range (HDR) imaging is an essential technique for overcoming the dynamic range limits of image sensors. The classic method relies on multiple exposures, which slows capture time, resulting in motion artifacts when imaging dynamic scenes. Single-shot HDR imaging alleviates this issue by encoding HDR data into a single exposure, then computationally recovering it. Many established methods use strong image priors to recover improperly exposed image detail. These approaches struggle with extended highlight regions. We utilize the global reset release (GRR) shutter mode of an off-the-shelf sensor. GRR shutter mode applies a longer exposure time to rows closer to the bottom of the sensor. We use optics that relay a randomly permuted (shuffled) image onto the sensor, effectively creating spatially randomized exposures across the scene. The exposure diversity allows us to recover HDR data by solving an optimization problem with a simple total variation image prior. In simulation, we demonstrate that our method outperforms other single-shot methods when many sensor pixels are saturated (10% or more), and is competitive at a modest saturation (1%). Finally, we demonstrate a physical lab prototype that uses an off-the-shelf random fiber bundle for the optical shuffling. The fiber bundle is coupled to a low-cost commercial sensor operating in GRR shutter mode. Our prototype achieves a dynamic range of up to 73dB using an 8-bit sensor with 48dB dynamic range.

Figures

Figures reproduced from arXiv: 2506.22426 by the authors.

**Figure 1.** System overview. (a) The scene is optically randomized then recorded using a global reset release (GRR) sensor, which encodes spatially varying [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Comparison between GRR and rolling shutter functions. (a) The GRR [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Examples of sensor measurements from our system (left) are ran [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (17 more)

**Figure 4.** Figure 4: Simulation measurements comparison after apply [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Baseline test comparison Q-score. Bar plot of Q-score reconstruction [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Reconstruction example of 1% saturation rate in the baseline test. The image includes the tonemap, 0 EV, and 4 EV of the ground truth and the reconstruction of HDR CNN, Deep Optics HDR, spatially varying exposure, and our method. Zoom-ins are displayed under each image…

**Figure 7.** Figure 7: Simulation results. We compare the ground truth generated by the multi-shot HDR to the reconstruction results using various forward models with a [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

**Figure 8.** Figure 8: Calibration system overview. Spatial points are displayed and se [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

**Figure 10.** Figure 10: Experimental results. We qualitatively compare our method against a multishot HDR method, a 14-bit raw image from Canon 5D Mark II, an 8-bit [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 12.** Figure 12: Relationship between isolated highlight density and Q-score. Each [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]

**Figure 13.** Figure 13: Reconstructions comparing static (left) and dynamic (right) ball. [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]

**Figure 14.** Figure 14: Depth sensitivity of the proposed system under increasing defocus. [PITH_FULL_IMAGE:figures/full_fig_p014_14.png]

**Figure 15.** Figure 15: Baseline test comparison Q-score. Bar plot of Q-score reconstruction [PITH_FULL_IMAGE:figures/full_fig_p016_15.png]

**Figure 16.** Figure 16: The photo of the prototype. The image system starts with a 50 mm [PITH_FULL_IMAGE:figures/full_fig_p018_16.png]

**Figure 17.** Figure 17: The reconstruction of ND filters with two orientations. (a) The [PITH_FULL_IMAGE:figures/full_fig_p018_17.png]

**Figure 18.** Figure 18: The comparison is between the unshuffled measurement and re [PITH_FULL_IMAGE:figures/full_fig_p019_18.png]

**Figure 19.** Figure 19: Simulation test images when we regard the optical matrix of the random fiber bundle as a neutral density filter. We display the tonemap, reconstruction, [PITH_FULL_IMAGE:figures/full_fig_p020_19.png]

**Figure 20.** Figure 20: Conceptual illustration of the effect of optical blur in the proposed [PITH_FULL_IMAGE:figures/full_fig_p020_20.png]

**Figure 22.** Figure 22: 1D signal sampling and reconstruction comparison between regular [PITH_FULL_IMAGE:figures/full_fig_p021_22.png]

**Figure 23.** Figure 23: The plot shows the Q-score of two simulated samples using permu [PITH_FULL_IMAGE:figures/full_fig_p021_23.png]

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Reference graph

Works this paper leans on

3 extracted references · 1 canonical work pages

[2012]

In2012 19th IEEE International Conference on Image Processing

High dynamic range video by spatially non-regular optical filtering. In2012 19th IEEE International Conference on Image Processing. IEEE, 2757–2760. Helge Seetzen, Wolfgang Heidrich, Wolfgang Stuerzlinger, Greg Ward, Lorne White- head, Matthew Trentacoste, Abhijeet Ghosh, and Andrejs Vorozcovs. 2023. High dynamic range display systems. InSeminal Graphics ...

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[2014]

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[2017]

Orazio Gallo, Natasha Gelfandz, Wei-Chao Chen, Marius Tico, and Kari Pulli

HDR image reconstruction from a single exposure using deep CNNs.ACM transactions on graphics (TOG)36, 6 (2017), 1–15. Orazio Gallo, Natasha Gelfandz, Wei-Chao Chen, Marius Tico, and Kari Pulli. 2009. Artifact-free high dynamic range imaging. In2009 IEEE International conference on computational photography (ICCP). IEEE, 1–7. Jinwei Gu, Yasunobu Hitomi, To...

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [2012]

In2012 19th IEEE International Conference on Image Processing

High dynamic range video by spatially non-regular optical filtering. In2012 19th IEEE International Conference on Image Processing. IEEE, 2757–2760. Helge Seetzen, Wolfgang Heidrich, Wolfgang Stuerzlinger, Greg Ward, Lorne White- head, Matthew Trentacoste, Abhijeet Ghosh, and Andrejs Vorozcovs. 2023. High dynamic range display systems. InSeminal Graphics ...

arXiv 2021

[2] [2014]

In2014 IEEE International Conference on Computational Photography (ICCP)

Single shot high dynamic range imaging using piecewise linear estimators. In2014 IEEE International Conference on Computational Photography (ICCP). IEEE, 1–10. Ahmet Oğuz Akyüz et al. 2020. Deep joint deinterlacing and denoising for single shot dual-ISO HDR reconstruction.IEEE Transactions on Image Processing29 (2020), 7511–7524. Ahmet Oğuz Akyüz and Erik...

work page arXiv 2020

[3] [2017]

Orazio Gallo, Natasha Gelfandz, Wei-Chao Chen, Marius Tico, and Kari Pulli

HDR image reconstruction from a single exposure using deep CNNs.ACM transactions on graphics (TOG)36, 6 (2017), 1–15. Orazio Gallo, Natasha Gelfandz, Wei-Chao Chen, Marius Tico, and Kari Pulli. 2009. Artifact-free high dynamic range imaging. In2009 IEEE International conference on computational photography (ICCP). IEEE, 1–7. Jinwei Gu, Yasunobu Hitomi, To...

arXiv 2017