BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement

Zishu Yao; Xiang-Xiang Su; Shengning Zhou; Guang-Yong Chen; Guodong Fan; Xing Chen

cs.CVMar 5, 2026

BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement

Authors: Zishu Yao, Xiang-Xiang Su, Shengning Zhou, Guang-Yong Chen, Guodong Fan, Xing Chen

Organizations: College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China · College of Computer Science and Technology, Shandong Technology and Business University, Yantai 264003, China

Abstract

Event cameras, with their high dynamic range, show great promise for Low-light Image Enhancement (LLIE). Existing works primarily focus on designing effective modal fusion strategies. However, a key challenge is the dual degradation from intrinsic background activity (BA) noise in events and low signal-to-noise ratio (SNR) in images, which causes severe noise coupling during modal fusion, creating a critical performance bottleneck. We therefore posit that precise event denoising is the prerequisite to unlocking the full potential of event-based fusion. To this end, we propose BiEvLight, a hierarchical and task-aware framework that collaboratively optimizes enhancement and denoising by exploiting their intrinsic interdependence. Specifically, BiEvLight exploits the strong gradient correlation between images and events to build a gradient-guided event denoising prior that alleviates insufficient denoising in heavily noisy regions. Moreover, instead of treating event denoising as a static pre-processing stage-which inevitably incurs a trade-off between over- and under-denoising and cannot adapt to the requirements of a specific enhancement objective-we recast it as a bilevel optimization problem constrained by the enhancement task. Through cross-task interaction, the upper-level denoising problem learns event representations tailored to the lower-level enhancement objective, thereby substantially improving overall enhancement quality. Extensive experiments on the Real-world noise Dataset SDE demonstrate that our method significantly outperforms state-of-the-art (SOTA) approaches, with average improvements of 1.30dB in PSNR, 2.03dB in PSNR* and 0.047 in SSIM, respectively. The code will be publicly available at https://github.com/iijjlk/BiEvlight.

Figures & tables

Figure 2: (a) Problems and challenges of existing methods, (b) The proposed BiEvLight framework.

Datasets		Image Only						Image + Event
Datasets		SNRNet	FourLLIE	Reformer	NeRCo	SCI++	URWKV	eSLNet	ELIE	EvLight	BiEvLight
Reference		CVPR’ 22	ACM MM’23	ICCV’ 23	ICCV’ 23	TPAMI’25	CVPR’ 25	ECCV’ 20	TMM’ 23	CVPR’ 24	BiEvLight
SDE-in	PSNR $\uparrow$	20.3482	19.7856	21.3169	19.4266	12.4683	21.5831	21.3622	22.0414	22.1880	22.8680
	PSNR* $\uparrow$	23.6785	23.2709	23.6502	23.2052	22.3026	22.6191	23.0825	23.6395	23.6940	26.0023
	SSIM $\uparrow$	0.6267	0.6176	0.6873	0.5626	0.4928	0.7168	0.7023	0.7083	0.7189	0.7750
SDE-out	PSNR $\uparrow$	21.5542	20.6323	22.3075	18.4125	12.0277	22.3732	20.6244	22.2167	22.4372	24.3599

Table 1: Quantitative comparison with advanced methods of low-light image enhancement on SDE and SDSD dataset. The red and blue represent the best and second-best values.

Figure 3: Qualitative results on SDE-in dataset.

Figure 4: Qualitative results on SDE-out dataset.

Figure 5: Visualization of Ablation Results for Event Denoising

Strategy	SDE-in			SDE-out
Strategy	PSNR	PSNR*	SSIM	PSNR	PSNR*	SSIM
Base	21.4295	21.9412	0.7024	21.8932	22.4921	0.6941
Base+ $x_{low}$	22.0431	24.6423	0.7423	23.0923	24.8432	0.7124
Base+ $x_{high}$	22.5398	25.5993	0.7532	23.8762	25.6823	0.7264
BiEvLight	22.8680	26.0023	0.7750	24.3599	26.1617	0.7451

Table 2: Ablation study of Event denoising.

Strategy	SDE-in			SDE-out
Strategy	PSNR	PSNR*	SSIM	PSNR	PSNR*	SSIM
Joint Learning	21.9812	24.2307	0.7356	22.8156	24.5623	0.7087
Alternating Learning	22.6785	25.3691	0.7698	24.1234	25.8945	0.7398
BiEvLight	22.8680	26.0023	0.7750	24.3599	26.1617	0.7451

Table 3: Ablation study of Bilevel learning.

Figure 6: Qualitative analysis of denoising hyperparameters $q$ and window size $W$ .

Datasets		Image Only						Image + Event
Datasets		SNRNet	FourLLIE	Reformer	NeRCo	SCI++	URWKV	eSLNet	ELIE	EvLight	BiEvLight
Reference		CVPR’ 22	ACM MM’23	ICCV’ 23	ICCV’ 23	TPAMI’25	CVPR’ 25	ECCV’ 20	TMM’ 23	CVPR’ 24	BiEvLight
SDE-in	PSNR $\uparrow$	20.3482	19.7856	21.3169	19.4266	12.4683	21.5831	21.3622	22.0414	22.1880	22.8680
	PSNR* $\uparrow$	23.6785	23.2709	23.6502	23.2052	22.3026	22.6191	23.0825	23.6395	23.6940	26.0023
	SSIM $\uparrow$	0.6267	0.6176	0.6873	0.5626	0.4928	0.7168	0.7023	0.7083	0.7189	0.7750
SDE-out	PSNR $\uparrow$	21.5542	20.6323	22.3075	18.4125	12.0277	22.3732	20.6244	22.2167	22.4372	24.3599

Table 4: Quantitative comparison with advanced methods of low-light image enhancement on SDE and SDSD dataset. The red and blue represent the best and second-best values.

Methods	SSIM	PSNR*	#Params	FLOPs	FPS	Train Cost
URWKV	0.716	22.61	2.25	36.67	11	56
eSLENet	0.702	23.08	0.19	471.81	11	28
ELIE	0.708	23.63	220.01	201.57	4	93
EvLight	0.718	23.69	22.73	194.24	18	34
Joint	0.735	24.23	2.471	61.58	24	19
Alternating	0.769	25.36	2.471	61.59	24	39

Table 5: Comparison of model parameters (M), FLOPs (G) and FPS (Frames) on

256\times 256

images, training time (Hours), and PSNR* on SED_in Dataset.

m	SSIM	PSNR*
0.005	0.772	25.85
0.01	0.775	26.00
0.05	0.771	25.92
0.1	0.772	25.76

Table 6: Sensitivity analysis of the scaling factor

m

on the SED_in dataset.

Figure 7: The network architecture of the proposed BiEvLight.

Figure 8: Qualitative results on SDE-out dataset.

Figure 9: Qualitative results on SDSD dataset.

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