Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex

Alexandru Brateanu; Tingting Mu; Codruta Ancuti; Cosmin Ancuti

cs.CVApr 11, 2026

Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex

Authors: Alexandru Brateanu, Tingting Mu, Codruta Ancuti, Cosmin Ancuti

Organizations: Department of Computer Science, University of Manchester, Manchester, United Kingdom · ETcTI, University Politehnica Timisoara, Timisoara, Romania · West University of Timisoara, Timisoara, Romania

Abstract

Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation. State-of-the-art (SOTA) LLIE techniques often rely on large models and multi-stage training, limiting practicality for edge deployment. Moreover, their dependence on a single color space introduces instability and visible exposure or color artifacts. To address these, we propose Multinex, an ultra-lightweight structured framework that integrates multiple fine-grained representations within a principled Retinex residual formulation. It decomposes an image into illumination and color prior stacks derived from distinct analytic representations, and learns to fuse these representations into luminance and reflectance adjustments required to correct exposure. By prioritizing enhancement over reconstruction and exploiting lightweight neural operations, Multinex significantly reduces computational cost, exemplified by its lightweight (45K parameters) and nano (0.7K parameters) versions. Extensive benchmarks show that all lightweight variants significantly outperform their corresponding lightweight SOTA models, and reach comparable performance to heavy models. Paper page available at https://albrateanu.github.io/multinex.

Figures & tables

Figure 2: Illustration of Multinex Architecture. Fusion modules $f_{\mathcal{L}}$ and $f_{\mathcal{R}}$ use illumination and reflectance guidance stacks $\mathcal{S}_{\mathcal{L}}$ and $\mathcal{S}_{\mathcal{R}}$ to produce color and luminance correction terms $\boldsymbol{\Delta}_{L}$ and $\boldsymbol{\Delta}_{R}$ . Retinex-like fusion then yields the Enhancement Delta $\boldsymbol{\Delta}_{I}$ .

		LOLv1 Dataset			LOLv2-real Dataset			LOLv2-syn Dataset			Model Size
	Method	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	Param. (M) $\downarrow$	GFLOPs $\downarrow$
	QuadPrior [ 38 ]	22.85	0.800	0.201	20.59	0.811	0.202	16.11	0.758	0.144	1252.75	1103.2
	GLARE [ 51 ]	23.55	0.863	0.086	22.51	0.871	0.105	18.21	0.842	0.178	59.48	508.42
	LLFlow [ 39 ]	21.15	0.854	0.116	17.43	0.831	0.176	24.81	0.919	0.067	17.42	358.4
Heavy ( $>10$ M)	GSAD [ 11 ]	22.77	0.852	0.102	20.15	0.846	0.113	24.47	0.929	0.051	17.36	442.02
	KinD [ 49 ]	17.65	0.775	0.207	17.54	0.669	0.375	18.32	0.796	0.252	8.02	34.99

Table 1: Results on LOLv1 [ 41 ] , and LOLv2 [ 45 ] datasets (real and synthetic) across four model groups of parameter scales. Best performance in each group is highlighted in bold .

Figure 3: Qualitative comparison of SOTA light-weight approaches [ 41 , 6 , 7 , 1 , 21 , 33 , 27 ] and Multinex on reference data.

	MEF		LIME		DICM		NPE		Mean		Model Size
Method	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	NIQE $\downarrow$	BRISQUE $\downarrow$	Params (M) $\downarrow$	GFLOPs $\downarrow$
SNR-Net [ 43 ]	4.14	31.28	5.51	39.22	4.62	37.35	4.36	26.65	4.66	33.63	26.35	4.01
LLFlow [ 39 ]	3.92	30.27	5.29	27.06	3.78	26.36	4.16	28.86	4.29	28.14	17.42	358.40
CIDNet [ 44 ]	3.56	13.77	4.13	16.25	3.79	21.47	3.74	18.92	3.81	17.60	1.88	7.57
ZeroDCE [ 7 ]	4.93	17.32	5.82	20.44	4.58	27.56	4.53	20.72	4.97	21.51	0.075	4.83
Multinex	3.46	13.82	3.76	14.60	3.64	16.39	3.71	12.52	3.64	14.33	0.0446	2.50

Table 2: No-reference results on MEF, LIME, DICM, and NPE using NIQE [ 29 ] and BRISQUE [ 28 ] . Lower is better. † indicates results obtained with newly trained models using the same architecture, training configuration, and evaluation protocol as the conference version.

Method	Bic.	Boa.	Bot.	Bus	Car	Cat	Cha.	Cup	Dog	Mot.	Ppl.	Tab.	mAP50%	P (K)
YOLOv3 [ 32 ]	79.8	75.3	78.1	92.3	83.0	68.0	69.0	79.0	78.0	77.3	81.5	55.5	76.4	–
KinD [ 49 ]	80.9	75.0	75.8	93.3	82.4	69.4	69.2	79.0	76.9	76.3	79.6	55.4	76.1	8000
ZDCE [ 7 ]	81.2	75.0	75.7	93.4	83.2	67.7	70.2	76.4	74.1	77.7	81.3	55.5	75.9	79
Pair [ 6 ]	80.8	78.3	76.8	90.5	84.5	66.8	69.1	75.6	78.9	73.7	80.3	54.5	75.8	342
DENet [ 31 ]	80.9	79.2	80.1	90.7	84.5	70.7	72.0	79.3	80.1	76.7	82.4	58.0	77.9	45
PE-Y [ 47 ]	84.7	79.2	79.3	92.5	83.9	71.5	71.7	79.7	79.7	77.3	81.8	55.3	78.0	91

Table 3: Low-light object detection on ExDark [ 22 ] : per-class AP% and overall mAP50% , with LLIE model parameter count ( P ). Best results are in bold , second best are underlined .

(a) Multinex Priors			(b) CWA/MSEF Analysis				(c) CWA and Attention Variants
Formulation	H-Dim	PSNR $\uparrow$	CWA	MSEF	H-Dim	PSNR $\uparrow$	Attention Operation	H-Dim	PSNR $\uparrow$
$\hat{\mathbf{I}}_{i}=f_{i}(\mathbf{I}_{i})$	87	14.15	✗	✗	55	19.92	CBAM ${}_{\text{S}}$	39	20.44
$\hat{\mathbf{I}}_{i}=f_{\mathcal{L}}(\mathcal{S}_{\mathcal{L}},\theta_{\mathcal{L}})+\mathbf{I}_{i}$	87	20.57	✓	✗	55	21.34	MHSA + Pooling	39	21.67
$\hat{\mathbf{I}}_{i}=f_{\mathcal{R}_{i}}(\mathcal{S}_{\mathcal{R}},\theta_{\mathcal{R}})+\mathbf{I}_{i}$	87	18.50	✗	✓	39	22.27	MDTA	39	22.39
$\hat{\mathbf{I}}_{i}=f_{\mathcal{L}}(\mathcal{S}_{\mathcal{L}},\theta_{\mathcal{L}})\odot f_{\mathcal{R}_{i}}(\mathcal{S}_{\mathcal{R}},\theta_{\mathcal{R}})+\mathbf{I}_{i}$	39	23.19	✓	✓	39	23.19	CWA	39	23.19

Table 4: Results of ablation studies (a), (b) and (c) validating design elements of Multinex.

Figure 4: Qualitative ablation on Multinex Priors study (a).

Figure 5: The example image used by Sec. B .

Figure 6: LRA visualization of chrominance candidates $\{[\mathbf{C}_{b},\mathbf{C}_{r}],[\mathbf{U},\mathbf{V}],[\mathbf{O}_{1},\mathbf{O}_{2}],[\mathbf{r},\mathbf{g}],\mathbf{S}\}$ .

Figure 7: Similarity heatmaps between all candidate grouped reflectance-guidance components.

Figure 8: LRA visualization of combinations of the chosen feature maps for the reflectance guidance stack $\mathcal{S}_{\mathcal{R}}$ .

Figure 9: Top Row: Candidate illumination feature descriptors $\{\mathbf{Y}_{\text{Rec.709}},\mathbf{Y}_{\text{mean}},\mathbf{Y}_{\text{YCgCo}},\mathbf{Y}_{\text{vmax}},\mathbf{Y}_{\text{lightness}},\mathbf{Y}_{\text{L}_{2}}\}$ . Middle Row: $\boldsymbol{\Delta}_{E}(c)$ maps of the same candidate descriptors, where $c\in\{1,2,\ldots,6\}$ . Bottom row: $\boldsymbol{\Delta}_{G}(c)$ maps of the same candidate descriptors.

Figure 10: Similarity heatmap between all candidate individual illumination-guidance components.

Prior	$\boldsymbol{\Delta}_{E}(c)\uparrow$ / Rank	$\boldsymbol{\Delta}_{G}(c)\uparrow$ / Rank	Avg. Rank
$\mathbf{Y}_{\text{Rec.709}}$	0.0132 / 1	0.0007 / 3	2.0
$\mathbf{Y}_{\text{vmax}}$	0.0107 / 2	0.0019 / 2	2.0
$\mathbf{Y}_{\text{L}_{2}}$	0.0012 / 6	0.0194 / 1	3.5
$\mathbf{Y}_{\text{lightness}}$	0.0029 / 4	0.0002 / 4	4.0
$\mathbf{Y}_{\text{YCgCo}}$	0.0038 / 3	0.0000 / 6	4.5
$\mathbf{Y}_{\text{mean}}$	0.0017 / 5	0.0000 / 5	5.0

Table 5: Importance ranking of candidate luminance priors. Higher values indicate greater unique contribution to the stack.

$\mathcal{L}_{\text{MSE}}$	$\mathcal{L}_{\text{MS-SSIM}}$	$\mathcal{L}_{\text{Perc}}$	PSNR $\uparrow$	SSIM $\uparrow$
✓	✗	✗	22.31	0.815
✗	✓	✗	19.04	0.820
✗	✗	✓	18.80	0.793
✓	✓	✗	22.43	0.830
✓	✗	✓	22.80	0.821
✗	✓	✓	19.74	0.838

Table 6: Ablation of loss functions.

$\mathcal{S}_{\mathcal{L}}$ Components				#Ch	PSNR $\uparrow$	SSIM $\uparrow$
$\mathbf{Y}_{\text{Rec.709}}$	$\mathbf{Y}_{\text{vmax}}$	$\mathbf{Y}_{\text{lightness}}$	$\mathbf{Y}_{\text{L}_{2}}$
✓	✗	✗	✗	1	18.80	0.753
✗	✓	✗	✗	1	19.10	0.744
✗	✗	✓	✗	1	19.23	0748
✗	✗	✗	✓	1	19.05	0.757
✓	✓	✗	✗	2	19.83	0.762

Table 7: Ablation studies on feature maps of luminance guidance stack, supported by a complete Multinex architecture.

$\mathcal{S}_{\mathcal{R}}$ Components			#Ch	PSNR $\uparrow$	SSIM $\uparrow$
$[\mathbf{C_{b},C_{r}}]$	$[\mathbf{r,g}]$	$\mathbf{S}$
✓	✗	✗	2	21.55	0.792
✗	✓	✗	2	21.90	0.805
✗	✗	✓	1	20.62	0.779
✓	✓	✗	4	22.98	0.835
✓	✗	✓	3	22.12	0.819

Table 8: Ablation studies on feature maps of reflectance guidance stack, supported by a complete Multinex architecture.

Placement	Formulation $f(\mathcal{S})=\mathrm{Conv}_{1\times 1}\circ\dots$	PSNR $\uparrow$	SSIM $\uparrow$
Before	$\mathrm{FB}^{2T}\Big(\mathrm{Conv}_{1\times 1}(\mathcal{S})\odot\mathrm{CWA}(\mathcal{S})\Big)$	22.78	0.831
Between	$\mathrm{FB}^{T}\Big(\mathrm{CWA}(\mathcal{S})\odot\mathrm{FB}^{T}\circ\mathrm{Conv}_{1\times 1}(\mathcal{S})\Big)$	23.19	0.843
After	$\mathrm{CWA}(\mathcal{S})\odot\mathrm{FB}^{2T}\circ\mathrm{Conv}_{1\times 1}(\mathcal{S})$	22.41	0.823

Table 9: Ablation on where to place CWA in fusion networks.

Panel of Figure 12 — Figure 12: Qualitative comparison on reference dataset LOL-v1 [ 41 ] between Multinex and state-of-the-art lightweight and micro scale models RetinexNet [ 41 ] , PairLIE [ 6 ] , ZeroDCE [ 7 ] , LYT-Net [ 1 ] , RUAS [ 21 ] , RSF [ 33 ] , SCI [ 27 ] .

Panel of Figure 13 — Figure 13: Qualitative comparison on reference dataset LOL-v1 [ 41 ] between Multinex and state-of-the-art lightweight and micro scale models RetinexNet [ 41 ] , PairLIE [ 6 ] , ZeroDCE [ 7 ] , LYT-Net [ 1 ] , RUAS [ 21 ] , RSF [ 33 ] , SCI [ 27 ] .

Panel of Figure 14 — Figure 14: Qualitative comparison on reference dataset LOL-v1 [ 41 ] between Multinex-Nano and state-of-the-art micro scale models RUAS [ 21 ] , RSF [ 33 ] , SCI [ 27 ] .

Panel of Figure 15 — Figure 15: Qualitative comparison on reference dataset LOL-v2-real [ 45 ] between Multinex and state-of-the-art mid-sized (1-10 M param.) models RetinexFormer [ 3 ] and CIDNet [ 44 ] , and heavy ( $>$ 10 M param.) model GLARE [ 51 ] .

Panel of Figure 16 — Figure 16: Qualitative comparison on no-reference datasets MEF [ 26 ] , LIME [ 9 ] , DICM [ 20 ] , NPE [ 36 ] between Multinex and state-of-the-art mid-sized (1-10M param.) models RetinexFormer [ 3 ] and CIDNet [ 44 ] and heavy-weight ( $>$ 10M param.) GLARE [ 51 ] .

Figure 17: Additional results on no-reference dataset MEF [ 26 ] . For corresponding images, top is input, and bottom is Multinex output.

Figure 18: Additional results on no-reference dataset LIME [ 9 ] . For corresponding images, top is input, and bottom is Multinex output.

Figure 19: Additional results on no-reference dataset DICM [ 20 ] . For corresponding images, top is input, and bottom is Multinex output.

Figure 20: Additional results on no-reference dataset NPE [ 36 ] . For corresponding images, top is input, and bottom is Multinex output.

Figure 21: A few challenging cases from DICM and MEF datasets.

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