Modern cameras correct color via two or three pre-calibrated 3x3 matrices interpolated by Correlated Color Temperature (CCT). This collapses the illuminant SPD to a scalar. LEDs, fluorescents, and mixed sources sit off the Planckian locus, and metameric illuminants with the same chromaticity demand different optimal CCMs that a CCT pipeline cannot distinguish. We recover the illuminant SPD instead using non-negative least squares against a basis dictionary, conditioned on the camera white point and a multi-channel ambient-light sensor (ALS). A closed-form 3x3 CCM is then fit per shot. On a synthetic benchmark of 300 illuminants and 3,100 reflectances, mean angular error drops from 1.89 to 1.20 degrees, with a 1.7x tail reduction on the LED-like off-Planckian group (n=45) where metameric ambiguity is tangible. The gain holds under realistic ALS imperfections and observation noise, and the same off-Planckian advantage carries over to 1,684 real measured SPDs.
Figures & tables
Figure 1: The metameric problem and our fix. (a) Two real EMPIR illuminants share xy chromaticity to within Δxy=0.003 but differ in normalized SPD shape by 43∘ . A CCT-driven CCM cannot tell them apart, yet they need different optimal CCMs. (b) On the LED-like Duv group of our benchmark ( n=45 , statistically meaningful), our spectral pipeline cuts worst-25% angular error from 5.3∘ (Adobe DNG) to 2.8∘ — a 1.9× reduction. A more extreme group ( ∣Duv∣≥0.03 , n=5 ) shows a larger directional gain that we report only for context.
Figure 2: Nikon D40 spectral sensitivity (filled bands) overlaid with the 14 -channel ALS filter bank used in our experiments.
Figure 3: EMPIR test illuminants on CIE 1931 xy , color-coded by signed Duv. The dataset spans Planckian sources, daylight phases, and significantly off-locus LEDs.
CCT-driven
xy -conditioned
Spectral (ours)
∣Duv∣ group
Oracle
DNG [ 1 ]
SELF
2D-NN
3×3 [ 2 ]
3×6
WP-only
WP + ALS
Mean angular error
Planckian <0.003 ( n=156 )
0.95
2.45
3.18
1.75
1.79
1.33
1.55
1.05
Mild 0.003−0.01 ( n=94 )
0.87
2.73
3.44
2.01
1.99
2.21
1.82
1.21
LED-like 0.01−0.03 ( n=45 )
0.81
3.09
4.26
2.78
2.41
2.27
2.18
1.31
Extreme ≥0.03 ( n=5 )
2.36
11.08
26.75
6.60
14.31
10.11
9.23
4.49
Table 1: Mean and worst-25% angular error (degrees) per Duv group on 300×3,100 (illuminant, reflectance) pairs after Bradford CAT to D65. 3×3 and 3×6 are the linear and root-polynomial heads of Tedla et al. ’s CST-MLP [ 2 ] ( 3×6 uses Finlayson root-polynomial features [ 8 ] ). Best non-Oracle entry per row in bold . The Extreme group has only n=5 illuminants; its worst-25% averages 1–2 samples and is reported for completeness, not statistical inference. Statistical weight rests on the LED-like group ( n=45 ). Paired bootstrap on the WP + ALS vs CST-MLP 3×6 mean gap (illuminant-level resampling, B=1000 ): overall Δ=+0.70∘ , 95% CI [+0.40,+1.19]∘ ; LED-like Δ=+0.97∘ , 95% CI [+0.67,+1.37]∘ ; Extreme Δ=+5.62∘ , 95% CI [+0.01,+14.56]∘ (the wide CI is the n=5 caveat made explicit).
Axis / condition
mean / worst-25 ( ∘ )
Baseline (ideal K=14 , no noise)
1.20/2.74
AS7341-like filters ( K=8 )
1.32/3.25
5% observation noise
1.23/2.83
OOD, EMPIR-only basis
2.75/5.40
OOD, basis +7 CIE refs
2.00/4.21
Table 2: Robustness of WP + ALS along four axes. Numbers are mean / worst-25% angular error in degrees on the relevant test set; for reference, DNG is 2.77/5.29 on the same scale (Table 1 ). Cross-DB OOD test set: 23 hold-out CIE reference illuminants (FL/HP/LED), with the bottom row using a basis pool augmented by 7 CIE reference SPDs.
Figure 4: Four real Nikon D40 captures. Top two rows near-Planckian (tungsten museum, daylight outdoor). Bottom two rows off the locus (LED hallway ∣Duv∣=0.012 , mall LED ∣Duv∣=0.021 ). Left column shows the scene with the chart highlighted. The four right columns show the 24-patch chart as-shot (raw camera response, before any white-balance, so each row shows its own illuminant cast), corrected by 2-CST DNG, by our spectral white-point pipeline, and the D65 ground truth. Per-shot mean angular error Δ relative to the D65 GT is annotated on the two corrected columns; the as-shot column is shown for context only. Spectral-WP edges out or matches DNG, with the largest gain on the most distant illuminant (tungsten, 1.46∘ vs. 2.36∘ ).
Method
Plk
Mild
LED
Ext
Mean angular error
DNG
2.12
1.91
2.51
6.97
CST-MLP 3×6
1.72
1.67
2.13
9.50
WP-only
2.67
2.31
3.16
5.76
WP + ALS
2.17
1.83
1.89
5.14
Worst-25% angular error
Table 3: Beyond RGB [ 18 ] mean and worst-25% angular error (degrees) by Duv group on 1,684 measured-SPD scenes. Groups: Plk = Planckian ( n=868 ), Mild ( n=473 ), LED = LED-like ( n=276 ), Ext = Extreme ( n=67 ). Best per row in bold . WP + ALS leads on LED and Ext in both metrics; the worst-25% advantage on LED is smaller than the mean advantage ( 0.17∘ vs 0.24∘ over CST-MLP 3×6 ), reflecting the broader-distribution test illuminants compared to the synthetic benchmark.
Color correction is a key component of camera image signal processing (ISP) pipelines, encompassing illuminant discounting and colorimetric mapping of device-dependent sensor responses to device-independent color spaces, such as CIE XYZ. Despite extensive research, accurate color correction remains challenging due to the non-linear relationship between camera sensor responses and CIE XYZ color space, as well as to the increasing presence of highly chromatic and spectrally complex LED illuminants. We propose a color correction framework based on illuminant-adaptive three-dimensional lookup tables (LUTs), which we call Color Correction LUT (C2LUT). Our method combines a chromaticity-aware illuminant representation with a non-linear color transformation, enabling accurate correction under illuminants spanning a wide range of chromaticities and spectral complexities. We employ Tucker tensor decomposition to represent the LUTs, ensuring that computational requirements remain sufficiently low for deployment in camera ISPs. In addition, we introduce a large-scale illuminants dataset comprising 1,473 spectral power distributions, with different chromaticities and spectral profiles. Experiments across multiple cameras, illuminants, reflectance datasets, and real captured images demonstrate consistent improvements over existing methods for color correction, reducing CIE ΔE00 by up to 20% and angular error by up to 18% while remaining compatible with modern camera hardware constraints. Code and datasets are available at https://github.com/claudiom4sir/C2LUT.
Claudio Rota, Luca Cogo, Simone Bianco +1
Department of Informatics, Systems and Communication University of Milano-Bicocca Milan, Italy
Traditional in-camera colorimetric mapping relies on correlated color temperature (CCT)-based interpolation between pre-calibrated transforms optimized for Planckian illuminants such as CIE A and D65. However, modern lighting technologies such as LEDs can deviate substantially from the Planckian locus, exposing the limitations of relying on conventional one-dimensional CCT for illumination characterization. This paper demonstrates that transitioning from 1D CCT (on the Planckian locus) to a 2D chromaticity space (off the Planckian locus) improves colorimetric accuracy across various mapping approaches. In addition, we replace conventional CCT interpolation with a lightweight multi-layer perceptron (MLP) that leverages 2D chromaticity features for robust colorimetric mapping under non-Planckian illuminants. A lightbox-based calibration procedure incorporating representative LED sources is used to train our MLP. Validated across diverse LED lighting, our method reduces angular reproduction error by 22% on average in LED-lit scenes, maintains backward compatibility with traditional illuminants, accommodates multi-illuminant scenes, and supports real-time in-camera deployment with negligible additional computational cost.
SaiKiran Tedla, Joshua E. Little, Hakki Can Karaimer +1
1York University · University of Toronto · 2AI Center-Toronto, Samsung Electronics
Recovering scene color from images captured in scattering media is a fundamental inverse problem in optical imaging. Yet the problem is intrinsically ill-posed as multiple solutions can explain the same observation, and prediction error cannot be controlled without understanding the space of candidate solutions. Here, we present sufficient conditions under which color recovery in a scattering medium becomes well-posed. Observing that ill-posedness stems from (i) projection of spectral signals onto pixel intensities, and (ii) unknown medium parameters, we demonstrate that sensor improvements alone cannot resolve medium-induced distortions without additional constraints. We identify recovery patterns, cross-pixel relationships that naturally occur in images, and prove, for an ideal hyperspectral camera, that they restrict the solution to a unique candidate. This opens the door to a new class of vision algorithms grounded in first principles, enabling quantitative analysis of images in scattering environments.
Grigory Solomatov, Derya Akkaynak
Hatter Department of Marine Technologies, University of Haifa, Haifa, Israel · Interuniversity Institute for Marine Sciences, Eilat, Israel