Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework

Hongru Han; Tingrui Guo; Liming Zhang; Yan Su; Qiwen Xu; Zhuohua Ye

cs.CVMar 26, 2026

Towards Controllable Low-Light Image Enhancement: A Continuous Multi-illumination Dataset and Efficient State Space Framework

Authors: Hongru Han, Tingrui Guo, Liming Zhang, Yan Su, Qiwen Xu, Zhuohua Ye

Organizations: Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China · School of Automation, China University of Geosciences, Wuhan 430074, China · Department of Electromechanical Engineering, University of Macau, Taipa, Macau 999078, China

Abstract

Low-light image enhancement (LLIE) has traditionally been formulated as a deterministic mapping. However, this paradigm often struggles to account for the ill-posed nature of the task, where unknown ambient conditions and sensor parameters create a multimodal solution space. Consequently, state-of-the-art methods frequently encounter luminance discrepancies between predictions and labels, often necessitating "gt-mean" post-processing to align output luminance for evaluation. To address this fundamental limitation, we propose a transition toward Controllable Low-light Enhancement (CLE), explicitly reformulating the task as a well-posed conditional problem. To this end, we introduce CLE-RWKV, a holistic framework supported by Light100, a new benchmark featuring continuous real-world illumination transitions. To resolve the conflict between luminance control and chromatic fidelity, a noise-decoupled supervision strategy in the HVI color space is employed, effectively separating illumination modulation from texture restoration. Architecturally, to adapt efficient State Space Models (SSMs) for dense prediction, we leverage a Space-to-Depth (S2D) strategy. By folding spatial neighborhoods into channel dimensions, this design allows the model to recover local inductive biases and effectively bridge the "scanning gap" inherent in flattened visual sequences without sacrificing linear complexity. Experiments across seven benchmarks demonstrate that our approach achieves competitive performance and robust controllability, providing a real-world multi-illumination alternative that significantly reduces the reliance on gt-mean post-processing.

Figures & tables

Fig. 1: Comparison between static “one-to-one” benchmarks (top) and our Light100 dataset (bottom). While traditional datasets are limited to a single ground-truth mapping, Light100 captures 100 progressive lighting levels. This allows for a flexible mapping governed by the target luminance level $\beta$ , accommodating various user preferences and ambient conditions.

Fig. 2: Visual comparison of state-of-the-art deterministic enhancement methods on the LOL-v2-Real dataset. Most advanced models produce outputs with a higher average luminance than the Test Ground Truth (GT), demonstrating a systematic luminance mismatch that stems from the deterministic mapping paradigm.

Fig. 3: Quantitative analysis of evaluation bias on the LOL-v2-Real dataset. (a) Luminance distribution analysis: outputs of SOTA methods tend to align with the global Training GT mean rather than adapting to the specific illumination of individual test samples. (b) The ”gt-mean” heuristic is commonly employed to evaluate the fidelity of texture and color by neutralizing the global luminance gap, leading to substantial PSNR gains. Here, w/o and w/ denote evaluations without and with this post-hoc adjustment, respectively.

Fig. 4: Overview of the Proposed CLE-RWKV Framework. (a) The overall pipeline operates in the decoupled HVI color space, where target luminance commands ( $\beta$ ) modulate features via FiLM. (b) The S2D-Net Backbone employs a split-transform-merge strategy, where the L-RWKV Block serves as the core processor. (c) Analysis of the Periodic Shuffle Strategy. Left: The PS-DS Embedding layer (Pixel Shuffle Downsampling with $r$ followed by $1\times 1$ Conv) demonstrates that semantic topology and features are preserved even at lower resolutions. Right: Visualization of the implicit 2D Token Shift. Compared to conventional 1D flattening, our periodic shuffle strategy folds spatial neighborhoods into the channel dimension, effectively bridging the “scanning gap” and enabling the L-RWKV block to capture local details while mitigating long-range forgetting.

Fig. 5: Resolving the Intensity-Chroma Conflict. (Left) Real physical captures at low illumination suffer from diminished SNR, manifesting as both stochastic noise and severe color shifts. (Middle) The hybrid target synthesizes the target-level intensity ( $\mathbf{I}_{max}$ ) with the pristine chromaticity and texture ( $\hat{\mathbf{H}},\hat{\mathbf{V}}$ ) extracted from the high-quality reference (Right). This strategy provides a noise-free, color-accurate guidance that decouples illumination control from intrinsic degradation restoration.

Method	LOL-v1			LOL-v2-Real			LOL-v2-Syn			Light100
Method	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS	PSNR	SSIM	LPIPS
LLFlow+gt-mean [ 9 ]	25.18	0.873	0.114	25.63	0.876	0.159	26.82	0.949	0.043	22.42	0.799	0.230
PyDiff+gt-mean [ 10 ]	27.19	0.882	0.109	27.10	0.881	0.150	26.63	0.929	0.070	21.83	0.805	0.191
LLFormer+gt-mean [ 11 ]	26.30	0.833	0.163	27.61	0.856	0.173	29.50	0.948	0.045	25.71	0.863	0.121
GSAD+gt-mean [ 12 ]	27.84	0.877	0.091	28.81	0.895	0.095	28.67	0.944	0.047	23.70	0.841	0.142
CIDNet+gt-mean [ 13 ]	28.20	0.889	0.079	28.14	0.892	0.101	29.56	0.950	0.047	25.48	0.869	0.087

TABLE II: Quantitative Comparison of CLE Paradigm. We compare our method with state-of-the-art LLIE approaches on four benchmarks: LOL-v1 [ 21 ] , LOL-v2-Real [ 23 ] , LOL-v2-Syn [ 23 ] , and our Light100. Note that all baseline methods were retrained on the Light100 dataset for fair comparison. Best and second-best results are highlighted.

Fig. 6: Visual comparisons on LOL-v1 (first row), LOL-v2-Real (second row), and LOL-v2-Syn (third row). The region within the red box is zoomed in and shown at the bottom left corner.

Fig. 7: Images of the same scene at different luminance levels are presented in rows two, four, and six, with the corresponding luminance histograms (red) positioned above each image. For a clearer comparison, the GT histogram (blue) is overlaid onto the corresponding histograms in each row. While linear scaling tends to amplify noise and introduce distribution bias into deterministic baseline models, the proposed CLE-RWKV method achieves accurate histogram alignment and superior perceptual fidelity.

Fig. 8: Efficiency Analysis on LOL-v2-Real. Each bubble represents a model, where the solid colored area corresponds to the peak memory footprint (labeled in MB) and the center white dot indicates the precise PSNR-FPS coordinate. All metrics are tested on an NVIDIA RTX 4090 with

3\times 256\times 256

input. Our CLE-RWKV (red bubble) achieves the optimal trade-off across all metrics.

Method	Configuration	Speed	LOL-v1		Light100
Method	Configuration	(fps)	PSNR	SSIM	PSNR	SSIM
Color Space	sRGB	-	27.06	0.871	29.07	0.902
Architecture	UNet	33.4	27.19	0.872	28.59	0.899
	S2D ( $P=1$ )	19.9	28.57	0.879	28.41	0.896
	S2D ( $P=2$ )	60.4	29.03	0.890	28.97	0.903
Loss Func.	$\ell_{1}$ Only	-	28.31	0.855	29.18	0.900

TABLE III: Ablation study. We evaluate each component’s contribution. Speed (FPS) is measured on an RTX 4090 with

3\times 256\times 256

input.

P

denotes the patch size. Best and second-best results are highlighted.

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Image Enhancement Low-Light Image Enhancement

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Existing deep learning-based low-light enhancement methods are typically trained on limited datasets with single enhancement targets, which restricts their generalization ability and controllability in real-world applications. To overcome these limitations, we propose ControlLight, a controllable, consistent, and generalizable framework for low-light enhancement. We first construct a large-scale dataset of real-world degraded images with continuous illumination-strength supervision. To further ensure consistent outputs under different control strengths, we introduce a misalignment-aware weighted flow matching loss that preserves image structure across continuous enhancement strengths. ControlLight allows users to edit real-world degraded low-light images toward satisfactory enhancement results by flexibly controlling the strength while preserving visual consistency and realism. Extensive experiments show that ControlLight achieves state-of-the-art performance against existing low-light enhancement approaches while demonstrating strong continuous controllability and generalization to real-world scenarios.

Abstract

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