BiEvLight: Task-Aware Event Refinement for LLIE

• The paper introduces a bi-level optimization framework that treats event denoising as task-aware refinement to improve low-light image enhancement.
• It couples a gradient-guided event denoising module with a multimodal enhancement network using a frozen Retinex decomposition and cross-attention fusion.
• Empirical results on SDE and SDSD datasets show notable gains in PSNR and SSIM, validating the approach’s effectiveness in reducing noise and preserving structure.

Searching arXiv for the BiEvLight paper and closely related event-guided low-light enhancement work. BiEvLight is a low-light image enhancement framework for event-assisted imaging that treats event denoising as a task-aware bi-level optimization problem rather than as a fixed preprocessing stage. The method is introduced in “BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement” (Yao et al., 5 Mar 2026). Its central setting is event-assisted low-light image enhancement (LLIE), where a low-SNR image captured under severe darkness is paired with an event stream, and the objective is to reconstruct a clean, bright, structurally faithful image. BiEvLight is motivated by the claim that existing event-based LLIE methods focus mainly on modal fusion while neglecting a dual degradation regime: low signal-to-noise ratio in images and intrinsic background activity noise in events. The framework therefore makes precise event refinement a prerequisite for effective event-guided enhancement, coupling denoising and enhancement through a bi-level formulation constrained by the downstream enhancement objective (Yao et al., 5 Mar 2026).

1. Definition and problem setting

BiEvLight stands for Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement (Yao et al., 5 Mar 2026). It addresses a multimodal LLIE problem in which a low-light image $x_{low}$ and a raw noisy event input $x_e$ are jointly used to estimate an enhanced image $\hat{x}_{high}$. The paper’s diagnosis is that prior event-based LLIE pipelines overemphasize fusion design and underemphasize the quality of the event signal itself. In the proposed view, event cameras are attractive in dark dynamic scenes because they provide high dynamic range and respond asynchronously to brightness changes, but their utility is limited by background activity noise, especially when the event contrast threshold is lowered in low light (Yao et al., 5 Mar 2026).

The paper models the event stream as

$x_e = \{e_k\}_{k=1}^{N},$

with each event represented as

$e_k = (i_k, t_k, p_k),$

where $i_k = (x_k, y_k)$ is the spatial location, $t_k$ is the timestamp, and $p_k$ is the polarity. Event triggering is described by

$$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$

with

$$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$

where $x_e$0 is the photocurrent, $x_e$1 is the event threshold, and $x_e$2 is a small positive constant (Yao et al., 5 Mar 2026).

A central claim is that the main bottleneck in real low-light event-guided enhancement is not only multimodal fusion, but the noise coupling created when a noisy image and a noisy event stream are fused directly. This motivates a two-part response: a gradient-guided prior for event denoising, and a bi-level optimization strategy in which the event denoiser is trained under the constraint of enhancement performance rather than only with a standalone denoising criterion (Yao et al., 5 Mar 2026).

2. Architectural organization

The framework contains an event denoising module $x_e$3, parameterized by $x_e$4, and a multimodal enhancement module $x_e$5, parameterized by $x_e$6 (Yao et al., 5 Mar 2026). The enhancement module is itself organized into three parts: a frozen pre-trained Retinex decomposition network $x_e$7, an illumination enhancement branch $x_e$8, and a reflectance enhancement branch $x_e$9.

The low-light image $\hat{x}_{high}$0 is first decomposed by $\hat{x}_{high}$1 into an initial illumination map $\hat{x}_{high}$2 and an initial reflectance map $\hat{x}_{high}$3. The illumination branch enhances $\hat{x}_{high}$4, while the reflectance branch enhances $\hat{x}_{high}$5 using refined events $\hat{x}_{high}$6. The raw event input $\hat{x}_{high}$7 is processed by the denoising network to produce $\hat{x}_{high}$8, and these denoised events are injected only into the reflectance branch. The enhanced image is reconstructed through Retinex recomposition: $\hat{x}_{high}$9 The paper interprets reflectance as the component that stores intrinsic object structure and detail, which is why event guidance is restricted to that branch (Yao et al., 5 Mar 2026).

Excluding the frozen decomposition network, the trainable subnetworks use unified 3-level U-shaped encoder-decoder architectures. The event denoising network predicts refined event maps trained by classification against pseudo-labels. The reflectance enhancement branch encodes denoised event input into multiscale event features $x_e = \{e_k\}_{k=1}^{N},$0, aligns them with image features $x_e = \{e_k\}_{k=1}^{N},$1, and fuses them hierarchically beginning at the bottleneck through an Event-Image Feature Fusion Block or Event-Image Attention Block. The supplementary description characterizes this fusion as cross-attention in which image features serve as queries and event features serve as keys and values: $x_e = \{e_k\}_{k=1}^{N},$2 where $x_e = \{e_k\}_{k=1}^{N},$3 are linear projections, $x_e = \{e_k\}_{k=1}^{N},$4 is a learnable scale, and $x_e = \{e_k\}_{k=1}^{N},$5 is conditional positional encoding via depth-wise separable convolutions (Yao et al., 5 Mar 2026).

3. Gradient-guided event denoising prior

A defining component of BiEvLight is its gradient-guided denoising prior. The method derives a relation between events and image structure through a brightness-constancy argument. The event stream is integrated over a temporal window $x_e = \{e_k\}_{k=1}^{N},$6 as

$x_e = \{e_k\}_{k=1}^{N},$7

with

$x_e = \{e_k\}_{k=1}^{N},$8

The event increment over $x_e = \{e_k\}_{k=1}^{N},$9 is written as

$e_k = (i_k, t_k, p_k),$0

Under brightness constancy,

$e_k = (i_k, t_k, p_k),$1

and with a Retinex-based approximation,

$e_k = (i_k, t_k, p_k),$2

leading to

$e_k = (i_k, t_k, p_k),$3

The paper uses this to argue that true events should align with strong reflectance gradients and motion, whereas background activity noise lacks such support (Yao et al., 5 Mar 2026).

This motivates a constrained denoising view: $e_k = (i_k, t_k, p_k),$4 where $e_k = (i_k, t_k, p_k),$5 denotes the gradient prior (Yao et al., 5 Mar 2026). In practice, pseudo-denoised event labels $e_k = (i_k, t_k, p_k),$6 are built using the gradient of the reflectance $e_k = (i_k, t_k, p_k),$7. The denoised event selection is defined as

$e_k = (i_k, t_k, p_k),$8

with mask

$e_k = (i_k, t_k, p_k),$9

and an adaptive threshold

$i_k = (x_k, y_k)$0

The paper describes this as a region-adaptive mechanism: textured regions admit stronger event support, while smooth regions are handled with a local threshold to reduce over-suppression (Yao et al., 5 Mar 2026).

The pseudo-label prior is not itself the final denoised event representation. Instead, it supervises the denoising network so that $i_k = (x_k, y_k)$1 learns a nonlinear mapping from raw events $i_k = (x_k, y_k)$2 to refined events $i_k = (x_k, y_k)$3. The denoising problem is formulated as a 3-class classification task over positive event, negative event, and no-event regions (Yao et al., 5 Mar 2026).

4. Bi-level learning formulation

The paper’s main methodological contribution is to recast event denoising as a bi-level optimization problem. The upper-level problem optimizes the denoising network $i_k = (x_k, y_k)$4, while the lower-level problem optimizes the enhancement network $i_k = (x_k, y_k)$5 conditioned on the current denoised events (Yao et al., 5 Mar 2026). The formulation is written as

$i_k = (x_k, y_k)$6

where

$i_k = (x_k, y_k)$7

and

$i_k = (x_k, y_k)$8

Although some typesetting in the paper is imperfect, the intended structure is explicit: the denoiser is optimized using both denoising supervision and enhancement performance induced by the lower-level optimum $i_k = (x_k, y_k)$9 (Yao et al., 5 Mar 2026).

The motivation is the over-denoising versus under-denoising trade-off. A task-agnostic denoiser may remove enhancement-relevant events or preserve too much noise. BiEvLight instead learns event representations specifically beneficial to LLIE. This is the basis for the paper’s description of event refinement as task-aware (Yao et al., 5 Mar 2026).

To avoid exact higher-order differentiation, the method uses a one-step truncated iterative differentiation approximation. The lower-level optimum is approximated by

$t_k$0

Let

$t_k$1

The upper-level gradient is then approximated by

$t_k$2

and the Hessian-vector product is estimated with finite differences: $t_k$3 where

$t_k$4

The main text uses

$t_k$5

while the supplement writes this more generally as $t_k$6 with $t_k$7 in experiments (Yao et al., 5 Mar 2026).

A common misconception addressed by the paper is that denoising can be treated as a static front-end before enhancement. BiEvLight explicitly rejects that view. The paper argues that static preprocessing “inevitably incurs a trade-off between over- and under-denoising and cannot adapt to the requirements of a specific enhancement objective,” hence the need for bi-level coupling (Yao et al., 5 Mar 2026).

5. Objectives, training protocol, and empirical performance

The enhancement loss is

$t_k$8

with $t_k$9 and $p_k$0. The denoising loss is a 3-class cross-entropy: $p_k$1 where $p_k$2 denotes positive event, negative event, and no-event region. In the bi-level formulation, the upper-level objective combines denoising and enhancement, while the lower level is driven only by $p_k$3 (Yao et al., 5 Mar 2026).

The training setup reported in the paper uses Adam on a single NVIDIA RTX 3090 GPU, with initial learning rate $p_k$4, batch size 8, random crop size $p_k$5, and random rotations by $p_k$6, $p_k$7, and $p_k$8. The bi-level step size follows cosine annealing with restarts, decaying from $p_k$9 to $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$0 over 150k iterations. For the gradient-guided prior, the supplement sets $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$1 and window size $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$2, and uses $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$3 for the finite-difference scale (Yao et al., 5 Mar 2026).

BiEvLight is evaluated on the SDE and SDSD datasets. SDE contains 91 paired image-event sequences, with 76 for training and 15 for testing; SDSD contains 150 paired sequences, with 125 used for training and 25 for testing. The original image resolution is $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$4, downsampled following EvLight to $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$5. The paper states that event streams are noisy and are synthesized with an event simulator (Yao et al., 5 Mar 2026).

On SDE-in, BiEvLight reports PSNR $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$6, PSNR* $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$7, and SSIM $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$8, compared with EvLight’s $$p_k = \left\{ \begin{matrix} +1 & \text{if } \bigtriangleup \eta > \epsilon \ -1 & \text{if } \bigtriangleup \eta < \epsilon \end{matrix} \right. ,$$9, $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$0, and $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$1. On SDE-out, it reports $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$2, $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$3, and $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$4, compared with EvLight’s $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$5, $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$6, and $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$7. The abstract summarizes the average gains on SDE as $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$8 dB in PSNR, $$\bigtriangleup \eta = \log \left( \frac{P(i_k)+b}{P(i_{k-1})+b}\right),$$9 dB in PSNR*, and $x_e$00 in SSIM (Yao et al., 5 Mar 2026).

On SDSD-in, BiEvLight reaches PSNR $x_e$01, PSNR* $x_e$02, and SSIM $x_e$03, while EvLight reports $x_e$04, $x_e$05, and $x_e$06. On SDSD-out, BiEvLight reaches $x_e$07, $x_e$08, and $x_e$09, compared with EvLight’s $x_e$10, $x_e$11, and $x_e$12 (Yao et al., 5 Mar 2026).

The supplementary also reports complexity: BiEvLight has 2.471M parameters, 61.59G FLOPs, and 24 FPS on $x_e$13 images. Training cost rises from 39 hours for alternating learning to 48 hours for BiEvLight, while the inference cost remains the same as the alternating variant (Yao et al., 5 Mar 2026).

6. Ablations, interpretation, and relation to adjacent low-light methods

The ablation studies are organized around the two defining ideas of the method: gradient-guided event denoising and bi-level optimization (Yao et al., 5 Mar 2026). On SDE-in, a base model without event denoising reports $x_e$14 for PSNR/PSNR*/SSIM, and on SDE-out reports $x_e$15. Adding denoising guided only by low-light image information (“Base+$x_e$16”) improves these to $x_e$17 and $x_e$18. Using higher-quality reflectance guidance (“Base+$x_e$19”) gives $x_e$20 and $x_e$21. Full BiEvLight further improves to $x_e$22 and $x_e$23 (Yao et al., 5 Mar 2026).

The optimization ablation compares direct joint learning, alternating learning, and BiEvLight. Direct joint learning yields $x_e$24 on SDE-in and $x_e$25 on SDE-out. Alternating learning improves to $x_e$26 and $x_e$27. BiEvLight gives the best results at $x_e$28 and $x_e$29. The paper interprets this as evidence that naive joint optimization introduces gradient conflict, while standard alternating learning does not provide sufficient cross-task feedback (Yao et al., 5 Mar 2026).

Relative to closely related event-guided LLIE methods, BiEvLight occupies a distinct position. “Bidirectional Image-Event Guided Low-Light Image Enhancement” introduces BiLIE, whose defining components are an Event Feature Enhancement module and Bidirectional Cross Attention Fusion (Liu et al., 6 Jun 2025). “Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset” introduces EIC-LIE, centered on Event-Illumination Collaborative Interaction and an Illumination-aware Event Filter (Xu et al., 21 May 2026). By contrast, BiEvLight’s explicit novelty is that event refinement is not treated as static preprocessing but as an upper-level problem optimized under the lower-level enhancement objective (Yao et al., 5 Mar 2026). This suggests a different axis of design emphasis: task-aware denoising rather than only fusion structure.

A further possible confusion arises with low-light methods whose names resemble “BiEvLight” but are not event-refinement frameworks. “Binarized Low-light Raw Video Enhancement” defines BRVE, a raw-domain binary neural network for low-light raw video enhancement (Zhang et al., 2024), and “Bilevel Generative Learning for Low-Light Vision” defines BGL, a RAW-to-RGB bilevel framework spanning enhancement, detection, and segmentation (Liu et al., 2023). These methods share either the low-light domain or a bilevel formulation, but neither is the BiEvLight event-assisted LLIE framework (Yao et al., 5 Mar 2026).

The paper’s limitations are partly explicit and partly structural. It depends on paired image-event data and meaningful cross-modal alignment; the supplement notes that pseudo-label generation uses ground-truth reflectance gradients during training; and the training procedure is more computationally involved because of truncated inner updates and finite-difference Hessian-vector approximations. The manuscript also does not provide extensive failure-case analysis, and some equations are not cleanly typeset (Yao et al., 5 Mar 2026). A plausible implication is that BiEvLight’s gains are most clearly established in regimes where the reflectance-event correlation is informative and synchronization is reliable. Within those assumptions, the method’s central contribution is to shift event-guided LLIE from fusion-centric design toward task-aware event representation learning, with event denoising optimized explicitly for enhancement quality rather than only for denoising fidelity (Yao et al., 5 Mar 2026).