FRBNet: Revisiting Low-Light Vision through Frequency-Domain Radial Basis Network
Authors: Fangtong Sun
Congyu Li
Ke Yang
Yuchen Pan
Hanwen Yu
Xichuan Zhang
Yiying Li
Venue: NeurIPS 2025 poster ()
Keywords: Low-light Vision
Invariant Feature Extraction
Frequency Domain Analysis
Primary area: applications

Abstract: Low-light vision remains a fundamental challenge in computer vision due to severe illumination degradation, which significantly affects the performance of downstream tasks such as detection and segmentation. While recent state-of-the-art methods have improved performance through invariant feature learning modules, they still fall short due to incomplete modeling of low-light conditions. Therefore, we revisit low-light image formation and extend the classical Lambertian model to better characterize low-light conditions. By shifting our analysis to the frequency domain, we theoretically prove that the frequency-domain channel ratio can be leveraged to extract illumination-invariant features via a structured filtering process. We then propose a novel and end-to-end trainable module named \textbf{F}requency-domain \textbf{R}adial \textbf{B}asis \textbf{Net}work (\textbf{FRBNet}), which integrates the frequency-domain channel ratio operation with a learnable frequency domain filter for the overall illumination-invariant feature enhancement. As a plug-and-play module, FRBNet can be integrated into existing networks for low-light downstream tasks without modifying loss functions. Extensive experiments across various downstream tasks demonstrate that FRBNet achieves superior performance, including +2.2 mAP for dark object detection and +2.9 mIoU for nighttime segmentation. Code is available at: \url{https://github.com/Sing-Forevet/FRBNet}.

FRBNet: Revisiting Low-Light Vision through Frequency-Domain Radial Basis Network
Fangtong Sun', Congyu Li", Ke Yang, Yuchen Pan, Hanwen Yu, Xichuan Zhang', Yiying Lit
Intelligent Game and Decision Lab (IGDL), Beijing, China edu. l cinogyuuhnu. edu. cn, zhxc@alu hit. edu. cn
(sunfangtong19, 1iylying10/Anudt. edu.
Abstract
Low-light vision remains a fundamental challenge in computer vision due to severe illumination degradation, which significantly affects the performance of downstream tasks such as detection and segmentation. While recent tf methods have improved performance through invariant feature learning modules, they still fall short due to incomplete modeling of low-light conditions. Therefore, revisit low-light image formation and extend the classical Lambertian model better characterize low-light conditions. By shifting our analysis to the frequency domain, we theoretically prove that the frequency-domain channel ratio can be leveraged to extract illumination-invartt We then propose novel and end-to-end trainable module named Frequency- domain Radial Basis Network (FRBNet), which integrates the channel ratio operation with a learnable frequency domain filter for the overall illumination invariant feature enhancement. As plug-and and-play module, FRBNet can be integrated into existing networks for low-light downstream tasks without modifying loss functions. Extensive experiments across various downstream tasks demonstrate that FRBNet achieves superior performance, including +2.2 mAP for dark object detection and +2.9 mloU for nighttime segmentation. Code is available at:
features via structured filtering process.
com com/Sing Forevet/FRBlet.
Introduction
recent years, computer vision tasks such as object detection 28 and semantic segmentation have achieved remarkable progress, driven by advances in deep learning techniques [19] (44 57 and
7 The models underlying these tasks typically trained well-lit, high-quality images [37 48 which often suffer from significant performance degradation when employed under low-light conditions. Morcover, available real- world low-light datasets [40] 733 remain relatively small in scale, hindering effecctive low-light network
the availability of large-scale annotated datasets [33 14
training.
To deal with low-light vision tasks, there are several mainstream methods: (1) Image enhancement methods, (2) Synthetic data training, (3) Multi-task learning strategies, and (4) Plug-and-play modules, as illustrated in Fig. (a). Image enhancement aims to restore visual quality before feeding images into downstream models. It can enhance visibility for humans but may not guarantee machine perception performance [64]. Synthetic data methods [3) address low-light data scarcity via image signal processes and other techniques, such as Dark ISP 100, but face high costs, limited diversity, and realism gaps. Multi-task learning jointly optimizes multiple objectives via complex loss functions
Equal Contributions. Corresponding Authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Synthetic Jon- -ight data generated by Dark ISP
Dork
Mecbal
kep
Welll
1. Prstrrniingg
Syeeheli tmegg
Piekune
Pre-kained Task Model
Ficeluee
Task Model
(Pretoii loss)
teage
Tost
Task Less
method
(2) Synthetic data training
Real-world low-light
Encoder
[
Module
Lowill
Becoder
Decoder 3
Reatoza
Model
Taus Lost
Wallt
Low Inag
Loss
(3) Multi-tusk learning strategy
(4) Plug-and-play modules
(a) Illustrative examples of four adaptation paradigms
(b) Comparison of synthetic and real data
Figure (a) Illustativve examples of four adaptation paradigms for low-light vision tasks, and (b) Comparison between synthetic low-light data (top) and real-world low-light data (bottom), demonstrating the higher complexity of real-world scenarios with localized light sources and non- uniform illumination patterns that synthetic methods struggle to accurately simulate.
but faces optimization challenges on large solution spaces. Unlike these, plug-an -nd--anddapaa -lay paradigms to enhance coumination-invariann features, such as DENet [46] and PE-YOLO [74], gain attention for their high applicability and flexibility adapt to various basic network architectures.
For plug-and-play paradigms, FeatEnHancer 21 improves low-light vision tasks via a hierarchical feature enhancement module. Subsequently, YOLA(5) employs zero-mean convolution extract inniination-invariinn features and achieves competitive performance. However, these methods lack complete modeling of low-light images in the real world, some of which are based incomplete assumptions such as the basic Lambertian model [29]. In addition, these spatial- convolution- based methods fall short in global perception due to fixed receptive fields.
Therefore, we revisit the imaging formation model and propose Bllug-and-pie module, termed Frequency-domain Radial Basis Network (FRBNet), for diverse low-light downstream tasks. Specifi- cally, inspired by the Phong illumination m5del we theoretically extend the classical Lambertian formulation (29 and construct extended generalized low-light model. Due to the limitations of the spatial-domane channel ratio, we propose frequency-domaii channel ratio and learnable frequency- domain filter based on optimized radial basis functions for illmination-invariia
feature extraction. Modulated by a zero-Direct Current (zero-DC) Gaussian frequency window and orientation angle, this filter forms an overall lightweight houg-and-play module for frequency suppression and structure filtering, Extensive experiments on four representative low-light vision tasks: object detection, face detection, semantic segmentation, and instance segmentation demonstrate that FRBNet significantly surpasses baselines and achieves superior performance.
The main contributions of this paper be summarized as follows.
We theoretically extend the Lambertian model for real-world low-light conditions and then formulate the novel Frequency-domale Channel Ratio (FCR) for coumination-invariiann feature enhancement. To the best of our knowledge, this is the first work that operates the channel ratio for clumination-invaraaa features in the frequency domain.
We design a Learnable Frequency- domain Filter (LFF) with a zero-DC frequency window and an improved radial basis filter for robust feature extraction. This filter can process unde- sired frequency components adaptively by frequency suppression and angular modulation.
Based on the theoretical analysis, we propose lightweight plug-and and-play module called lisquency-dommalee Radial Basis Network (FRBNet) which can be seamlessly integrated into various low-light vision tasks. It provides a frequency-domain illuminationninvaiit feature enhancement paradigm through the inter-relationships of channels constructed by FCR and the effective filtering by LFF. Comprehensive evaluations demonstrate that FRBNet outperforms existing ofte-offtheean methods on various low-light vision downstream tasks.

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2 Related work
2.1 Low-light vision for downstream tasks
Beyond direct image enhancement approaches 62 77 49 72 31], recent research has explored alternative strategies for improving downstream vision tasks in low- -light conditions. Several works leverage synthetic data generation to address the scarcity of real low-light datasets. DA [13] simulates low-light conditions through image signal processing to achieve zero-shot adaptation of detectors. WARLearn [ uses unlabeled synthetic data to enhance representation learning for adverse weather robustness. Similarly, BrightVO [59] generates synthetic low-light data through CARLA [2] simulation to train brightness-guidee guided Transformers for visual odometry tasks. Another
paradigm involves joint enhancement and detection via multi-task learning [10]
24 27). Recent
benchmarks like RealUnifyy [53] explore if cross-task unified vision models consistently benefit performance. End-to-end optimization methods directly target downstream task performance rather than intermediate image quality [74] [38]. DENet [46] and FeatEnHancer [21] focus on feature-level
enhancement through learnable modules integrated into detection networks. Subsequently, YOLA [5] extracts illumination-inwe invvaiiat features through channel-wise operations, directly improving detection performance in low-light conditions. We share the philosophy of end-to-end optimization; however, we note that existing approaches often overlook the complexity of real-world low-light scenarios, such local light sources and uneven reflections, which are explicitly considered in our design.
2.2 Frequency -omaan analysis in low-light image processing
by sep- arating illumination from structural details through spectral decomposition. T low-frequency components represent global illumination and smooth variations, while high-frequency captures edges and textures [60]. LouILLIE (58) utilizes amplitude information to enhance brightness and recover details in two-stage framework. Similarly, Frequency-ANwe -wware Network (52) selectively adjusts lowffrequency components while preserving high- gighffreueeccy details. Li et al. (32 employ frequency decomposition to guide hybrid representations for joint image denoising and enhancement. In the realm of generative models, FourierDiff[42] embeds Fourier priors into diffusion models for zero- shot enhancement and deblurring, while FCDiffusion [88] enables controllable generation through frequency band filtering. Beyond enhancement, Freq 79) integrates frequency analysis with the Mamba architecture for effective image deraining. However, most existing frequency-dome approaches mainly operate the pixel level by modifying low-frequency illumination components while preserving high-frequency details. In contrast, our method is the first to leverage channel ratio representations for extracting Illuminationiivarrtc features directly in the frequency domain, shifting the focus from pixel-level enhancement to feature-level learning.
Frequeney-domain analysis has proven effective low-light image enhancement 78 23 35
3 Theoretical Analysis of Method Design
3.1 Extended generalized low-light model
The classical Lambertian image formation model (29 51 characterizes low-light scenarios through
the diffuse reflection assumption (8), expressing an image at pixel location (æ, y) as:
(c y) = m|A(r, y). [(z, v)] *e (z, v) (I,
(I)
Here, C (R, B) denotes the RGB color channel. ń and i rrepresent the surface normal and light direction, respectively. m[.-)] -] the interaction function, yPc denotes the illumination component, pc denotes the intrinsic reflectance component.
The Lambertian model assumes purely diffuse reflection, where light is scattered uniformly across the surface. However, real-world low-light images (Fig. (T])) frequently contain complex and spatially localized light sources, including streetlights, vehicle headlights, and neon signs. These sources contradict the idealized diffuse reflection assumption underlying the Lambertian model.
Motivated by the additive decomposition in the Phong illumination model [45) A.I for details), introduce an extended version of the Lambertian model adapted to real-worke low-light scenes by reinterpreting the localized light sources non non-uniform highlights, which can be expressed as:
lo(æ, v) m m/ii,, v), Üe v)] (p v) pa(x, y) + +S((x,)).
(2)
3

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where Se represents spatially irregular highlight component that can be further defined as:
Sc y) Hz v) mlu(2z y). Mx, y)] (P( y) Pc v),
(3)
with He denoting the relative strength of highlight interference. For notational simplicity, we define Dc(x. v) = m [1, y). I(. y)] (pc pc(x, y) as the standard diffuse reflection component. Substituting this into Eq. (2 and rearranging terms, we obtain a more concise expression:
Ic v) Dc(a, 3) + Sc y) = Dc(a, (1+ Hc(x,)))
(4)
3.2 Frequency-domain channel ratio
Leveraging channel ratios (CR) isolate illumination-invarian features has proven effective for low-light visual tasks 44 17 S.. Taking the channel ratio between the red channel It and the green channel G as an example, the log-transformed formulation, according our extended generalized low-light model, can be obtained as:
IR = log
PR PR (1 + HR)
CR = log
1G
PG PG (1 Hg)
(5)
log n - log ya + log/n pn - log PG + log(1 + Hn) log(1 + Ha)-
As shown in Eq. the nonlinear residual from the highlight term disrupts the clean separation of illumination and reflectance, limiting the effectiveness of spatial-domain channel ratio methods. To overcome these limitations, we shift analysis to the frequency domain, where illumination and reflectance components naturally occupy different frequency bands (60), enabling more effective separation of illumination-invarial features. Drawing inspiration from prior works dommial we innovatively propose the Frequency-domal Channel Ratio (FCR) as:
channel ratios
44
17 51
T Ig
FCR Fllog(
(6)
= Fllog &n log pc] + Fillo pn - log pc] + Fllo((1 + Hn) - log(1 + HG)],
where F[.] represents the Fourier transform operator. To handle the non-linear residual term F[log(1 + HR) log(1 Hc]], apply first-order Taylor expansion. Given that significant contributions in the data are usually sparse and localized, we assume that He E [0, 1) has a relatively small magnitude, allowing
approximate log(1 Hc) Hc O(H))-
Under the aforementioned assumption, by neglecting higher-order terms, we can obtain a linearized approximation of A as follows:
F(HR - Ha] = HR - Ho.
(7)
where Hn and He denote the frequency-domain representations of Hn and Ho, respectively. To investigate the spectral characteristics of the residual term A, we decompose it into its amplitude and phase components:
= HR Hr ag gg0n ag eic
(8)
where ag, ag represent the amplitude terms, and 0R. 0g denote the phase components. To charac- terize the phase relationship between channels, we introduce the frequency correlation coefficient CornG = di ( (derived from AA2), see (56)), which quantifies the angular displacement between channel responses in the frequency domain. This allows reformulate A
A = :0R - (AR ag -a0--a---0000000000 e((0000)) =00 (aR ag CorRG),
This factorization reveals that the residual term is structured as a phdsl-modulated component, where iinn serves the carrier phase and (aR- -ag- -ag-Cornee encodes the amplitude discrepancy modulated by the inter-channel phase correlation.
Finally, the ultimate formulation of the frequency-domain channel ratio can be summarized as:
FCR Fllog PR log pPl + F(log PR log PG]
+ e 10 (aR aG Cor
(10)
illumination
reflectance
high-lit residual
Leveraging on the inherent properties of the spectral separation and chase-modulatte structure of residual terms, we design specialized filtering strategies that aim to robustly extract invariant illumination features, thus enhancing the reliability and effectiveness of feature extraction under varying lighting conditions.

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Frequency-domain Radial Basis Network
Red Channd
Impat Image
Fourier Transform
Concat
Frugsoncy-damail domaain Chaunel Rails
(FCR)
Fiber
1xx,y)
Us
(neelc)
(.1)
Fine Mediee
Grein Chann!
Lageribesic Transbient
W H(u. 1 1917
D81
(u.
Low-light Downstream Task
Conv DPS
Come PP3)
Com (P
Fout
F
16.22
Conv
Fuse Module
Figure 2: The overall pipeline of our proposed FRBNet. It performs Illumiaation-invariant feature enhancement process in frequency domain using core learnable filter for downstream low- low-light vision tasks.
Our theoretical analysis presented in Section 3.2 reveals that illumination interference predominantly accumulates within the low-frequency components of the signal. In contrast, the residual interference manifests as deeeedenndeeendentt patterns, which are distinctly characterized by phase modulation. Thus, illumination invariant features in real-world low-light images can be enhanced by suppress- ing fluctuating illumination interference and high-lit residual terms. To this end, we propose the Frequency-domait Radial Basis Network, which is lightweight plug-and-play module as illustrated Fig. 2 In this section, we will first introduce the whole caaminationninvariian feature enhancement process based on frequency-domat channel ratio, and then provide detailed description of the core learnable requeecy-domall filter.
4.1 Illmination-invarian feature enhancement process in the frequency domain
To enhance illumination-invarian features, the proposed FRBNet first converts the operation of channel ratio to the frequency domain. Inter-channel relationships are exploited in the frequency domain according to the FCR function presented in Section [.2 Define the input image in the spatial domain as [(x, y). for each channel pair, FCR is implemented by the frequency-domain logarithmic difference with learnable frequency parameters (u, v) as:
dir (u, v) Flog Iπ( 3)] Filog (x v)]
diron (u, v) = Filog (a v)] Filog (n y)]
(1))
dir v) = Fllog 7 (2, v)] F(log [x(
Next, Learnable Frequency-domain Filter, defined LFF, is designed to reduce the impact of illumination and high-lit residual terms in low-light images on robust feature extraction for each channel pair, which is composed of a zero-DC frequency window and an improved radial basis filter. The frequency response feature F Fin (u, v) can be expressed as:
P (u, v) LFIRG (u, v) dir (u,v) FGB
inv (u, v) = LFFOB (u, v) dirGn (u,
(12)
F.BR (u,v) v) = T.FFBBB( u) dir BR
(u,
Then, the filtered spectral features are transformed back to the spatial domain. The resulting features of all channel pairs (R G. G & B, B & R) are concatenated as:
F Fine (I. y) Cat (F
RG (u, u)] F-1 FF (u, :.: (u, e)]) inv
(13)
where F represents the inverse Fourier transform and Cat represents the concatenation operation. To further combine the enhanced illumination invariant features from the frequency domain with the spatial-domain features from the original image, common fuse module referring to [5) is employed
for integration as:
F Fout Conv {CB [Cat (CB(F)n (z, v)]: CB[I(a, y)]]]}
(14)
5

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where Conv is a convolution while CB is a Convolution followed by Batch Normalization (BN). Finally, the output feature out fed into the downstream task network.
4.2 Learnable frequency-domain filter
The core of our approach is the Learnable Frequency-domain Filter (LFF) that adaptively processes spectral components. This filter consists of two complementary elements: zero-DC frequency window Wg g that attenuates low-frequee wwwfrreuency illumination and an improved radial basis filter H v) that encodes both spectral distance and directional information, which can be formulated as:
LFF v) Wg H Hu,.
(15)
Zero-DC Frequency Window. To suppress undesired illumination while preserving structural information, a Gaussian window is employed centered the origin of the frequency plane as:
r(u, v) 0
Wg(u, v) = exp
r(u, v) = u2 v²,
(16)
where ow is learnable bandwidth parameter, and r(u, v) denotes the normalized radial frequency coordinate. To eliminate the DC component, (O, 0) is explicitly set to O, which ensures the filter to remove global brightness offsets while retaining mid- to high-frequency information for local structural cues.
Improved Radial Basis Filter. To construct a spectrally adaptive and directionally selective filter, we employ set of learnable radial basis functions (RBFs) combined with angular modulation. RBFs can capture maggntude selectivity, whereas angular terms can introduce orientation sensitivity to enable anisotropic filtering in the Fourier domain. Define set of K radial basis functions 6(u, v)
centered at predefined frequency radii μı [0, 1] as:
(u,u, v) cxp (- (r(u,, v) 1xx)
k = 11,1,,2 ,K]
(17)
20
where r(u, v) is the normalized radial frequency as defined earlier, and dr is learnable bandwidth parameter shared across all bases. With learnable coefficients a of the weighted linear combination, the final radial response is:
(u,, Σ a dh (u, [1,2 .K]
(18)
Furthermore, referring to the phase-orientee residual structure in Section 3.2 the interference term exhibits dominant orientation components. The radial response further modulated by an angular term constructed from sinusoidal harmonics of orientation angle to capture directional selectivity as:
(u=+)) (19)
M(n,e) v) = + cco((n((,, v)) + sin(nf(u,, e))], O(u, v) arctan
where N is the number of angular frequencies and À controls the modulation strength. The final frequency-domain radial basis filter response is given by:
H(u, v) = (u, v) M(u,
(20)
By integrating angular harmonics, the improved radial basis filter is both spectrally localized and di- rectionally responsive, enabling to alignment or suppression of such oriented residuals in a data-driven manner, which is crucial for isolating illumination-invaiian features while attenuating structured interference.
5 Experiments
We conduct extensive experiments to evaluate the effectiveness of the proposed plug-and-ple FRB- Net low-light vision tasks of detection and segmentation. Specifically, we adopt ExDark[[0),

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DarkFace 73), ACDC-night and LIS[4] datasets for dark object detection, face detection, night- time semantic segmentation, and dark instance segmentation tasks, respectively. Experiments are implemented based on the KMDetection [2] and MMSegmentation 6 toolboxes by PyTTrrch and trained on a NVIDIA RTX 4090 GPU. We select several recent representative methods for compre- hensive comparison in each task. For fair comparisons, the number of radial basis functions K is set to and the angular modulation strength is set to 0.1. Standard metrics including Recall, mAP, and mloU are adopted for evaluation. More details for each task can be found in Appendix A.3
5.1 Low-light detection tasks
Settings. We evaluate FRBNet low-light detection tasks using two representative detectors: YOLOv3 47 and TOOD [66] Both detectors are initialized with COCO-pretrained weights and fine-tuned with FRBNet as plug-in frontend on low-light datasets. We select representative methods from four paradigms for comparison: enhancement- based approaches, synthetic data training, multi- task learning, and boug-and-pla modules. Following the experimental setup of YOLA [5,, we set the momentum and weight decay of the SGD optimizer for the detection model to 0.9 and 0.0005, respectively. The learning rate is 0.001. For Exlarkk all input images resized 608 608, and both detectors are trained for 24 epochs. For DarkFace, YOLOv3 maintains 608 x 608 and is trained for 20 epochs, while TOOD uses higher resolution of 1500 1000 and is trained for 12 epochs.
Results of Object Detection. On the ExDark dataset, FRBNet consistently improves performance over baseline detectors and achieves the best mAP (see Table I. Specifically, our method attains 90.6% Recall and 74.9% mAP with YOLOv3, surpassing the previous state-of-the-ar YOLA by 0.4 mAP. When integrated into TOOD, FRBNet further boosts performance to 93.2% Recall and 75.3% mAP, outperforming all enhancement beseeeeeeettteeeddddd and multi-task approaches. For a fair comparison, part of the experimental results from YOLA [5]. These results demonstrate the effectiveness. of our frequency-domain design in preserving structural cues under illumination degradation.
Results of Dark Face Detection. Consistent with the official experimental setup in UG2+ Challenge we adopt a 3:1: random split the DarkFace dataset for training, validation, and testing in our experiments. Table presents FRBNet achieving strong performance across both detectors. It obtains 75.7% Recall and 57.7% mAP with YOLOv3, outperforming all previous plug-and-play and enhancement-basee methods. For TOOD, our module improves detection performance to 82.7% % Recall and 65. mAP, setting a new state-of-the-as and exceeding the previous best (YOLA) by 2.0% mAP. These gains highlight the generality and robustness of FRBNet across different detectors.
Table 1: Quantitative results low-light object detection and face detection on ExDark [4) and DarkFace 773. Bold values indicate the best results, while underline values represent the second-best.
ExDark
DarkFace
Paradigm
Method
YOLOv3
TOOD
YOLO.3
TOOD
Recall
mAP
Recall
mAP
Recall
mAP
Recall
mAP
Baseline
71.0
91.9
73.8
54.8
80.9
57.0
SMG(05KCVPR-23)
82.3
68.5
91.8
71.5
73.4
52.4
80.2
56.3
NeRCo (CCC-223)
83.4
68.5
91.8
71.8
73.8
53.0
79.4
56.8
Enhancement
Lighiiin (ECCV.24
84.3
71.3
92.1
72.9
75.5
57.4
81.0
58.7
DarkIR (CVPR-25)
81.9
68.2
90.9
72.0
74.5
55.9
$1.4
60.4
DAINer (CVPR-24)
86.7
73.4
74.8
56.9
Synthetic Data
WARLeam (WACV225)
56.2
85.1
72.5
92.5
74.7
55.7
59.6
Multi-task
IAT g (BMVC-22)
85.0
72.6
92.9
73.0
73.6
55.5
79.7
58.3
(EN
84.2
71.3
92.6
73.5
71.8
52.6
73.6
49.6
Plug-ond-play
TeatEnllancer 2Ccc
90.4
71.2
96.4
74.6
74.1
55.2
81.7
60.5
YOLAS\WWeurPPS22)
86.1
72.7
93.8
75.2
74.9
56.3
83,1
63.2
FRBNetiours)
90.6
74.9
93.2
75.4
75.7
57.7
82.7
65.1
5.2 Low-light segmentation tasks
Settings. We assess the ability of FRBNet to perform low-light segmentation tasks in nighttime semantic segmentation and dark instance segmentation. For the semantic segmentation task, the input images of ACDC-Night [50] are resized to 2048 1024. DeepLabV3+ [ is adopted as the bascline
7

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with a ResNet-50 [55] backbone, which is initialized with ImageNet-pretee pretrained weights [.. We compare our FRBNet with current state-of-the-an methods. Following the experimental protocol of FeatEnHanneer [21]. all methods are trained for 20K iterations. The comparison includes traditional enhancement-bange based methods as well as more recent task-oriented approaches. For the instance
segmentation task, the input images of the LIS dataset 4 are resized to 1330 800. Mask R-
CNN [22] with a ResNet-50 backbone implemented via the MMDetection framework [2] is employed
as the baseline model. MBLLEN [4], DarkIR [15] FeatEnHancer [21]. and YOLA [5] are selected as comparative models. All models are trained for 24 epochs using the SGD optimizer.
Table 2: Quantitative results of low-light semantic segmentation the ACDC 50). The symbol set WLBBU.WA,F..PO.TL.S...
TE..SK,E represents {road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, car, train, bicycle).
Method
RO
S1
BU
WA
FE
PO
TL
TS
VE
TE
SK
PE
CA
TR
Bi
mlo
Baseline
90.0
61.4
74.2
32.8
34.4
45.7
49.8
31.2
68.8
14.6
80.4
27.1
62.1
76.3
14.4
50.8
RetinexNet Mi
89.4
61.0
70.6
30.1
28.1
42.4
47.6
25.7
65.8
8.6
77.3
21.5
54.8
67.4
8.2
46.5
65
90.5
61.5
72.8
31.9
32.5
44.5
47.3
27.2
65.7
10.2
76.5
24.2
55.4
71.1
11.9
48.2
FIDE 71
90.0
60.7
72.8
32.4
34.1
43.3
47.9
26.1
67.0
13.7
78.0
26.5
57.1
71.0
12.4
48.8
KinD 75
90.0
61.0
73.2
31.9
32.8
43.5
42.7
27.7
65.5
13.3
77.4
22.8
55.1
74.5
11.5
48.1
EnGAN
89.7
58.9
73.7
32.8
31.8
44.7
49.2
26.2
67.3
14.2
77.8
25.0
59.0
71.2
7.8
48.6
ZcreDCB20
90.6
59.9
73.9
32.6
31.7
44.3
46.2
25.8
67.2
14.6
79.1
24.7
59.4
66.8
13.9
48.7
SSIENee 76
89.6
59.3
72.5
29.9
31.7
45.4
43.9
24.5
66.7
10.6
78.3
22.8
52.6
71.1
5.4
46.9
af. 68
78.4
43.8
38.8
21.9
61.6
85.2
36.1
FeutEnnHancer
70.6
75.6
41.8
35,9
68.5
61.8
80.0
56.0
YOLA
93.2
72.1
29.3
41.1
39.1
00.4
44.4
71.5
4.7
83.2
37.8
66.8
85.0
49.2
58.7
FRBNet(ours)
94.4
75.5
79.7
46.0
45.4
52.3
64.9
50.8
72.2
9.5
84.2
40.9
70.4
88.7
49.3
61.6
Results of Semantic Segmentation. Table summarizes the quantitative results the ACDC-Night benchmark. Since the testing set of ACDC- ACgh-Night contains some extremely rare samples, we report the quantitative results of IoU on 15 categories, excluding truck, bus, rider, and motorcycle. And the results of mloU are adopted directly from the output of MMSegmentation toolbox. From Table 2 most of the existing enhancement methods yield only marginal improvements. Compared with YOLA (58.7%), FRBNet further improves to 61.6% mIoU, achieving the best result. Notably, FRBNet delivers consistent gains across multiple key classes in nighttime semantic segmentation. such as sidewalk (75.5%), building (79.7%). and traffic sign (50.8%). The second line in Fig. reveals that the visualized results of FRBNet are the most similar to the ground truth.
3|a) also
Results of Instance Segmentation. Following common practice, we evaluate instance segmentation performance with mAP, mAP so, and m mAP75 metrics. As shown in Table 3 FRBNet achieves the best performance across all metrics on the LIS. It obtains 30.2% mAP, 50.5% mAP30. 30, and 30.4% mAP75. outperforming previous methods by a clear margin.
Table 3: Quantitative results of low-light instance segmentation on the LIS 4
Table 4: Ablation study on the effectiveness of each component in FRBNet.
Method
mAPs0
mAP15
H(u,r)
Wr.
FCR
ExDark
DarkFace
Mask RCNN 22
23.7
41.5
23.3
Buseline
71.0
57.0
MBLLEN
41
22.5
40.7
22.3
72.5
62.0
DarkIK 15
27.4
46.3
27.5
YOLA
24.9
44.8
24.2
Ablation Cases
4
72.9
62.5
FendEnHancer 211
29.1
48.7
29.7
73.5
63.7
FRBNetiours)
30.2
50.5
30.4
FRBNet
74.9
65.1
5.3 Ablation studies
Effectiveness of each component. We evaluate each component of FRBNet with YOLOv3 on ExDark and TOOD DarkFace. Specifically, the channel operation in the frequency domain (FCR) and the two elements of LFF are evaluated. As Table the proposed FRBNet adopting the whole LFF and FCR presents superior performance, and FCR plays more relatively important role.
analysis. Table compares FRBNet with existing methods for the effec- tive balance of computational efficiency and performance in low-light vision applications. Non-
8

---

architectural methods, which enhance performance through preprocessing pretraining without modifying the detector structure, show limited performance despite their high computational effi- ciency. For end-to-end modules, FRBNet achieves the highest detection performance (74.9 mAP on ExDark using YOLOv3) and segmentation accuracy (61.6 mloU) at a relatively low computational cost. FRBNet also demonstrates strong inference speed (89.5 FPS), significantly faster than others like FeatEnHancer (33.1 FPS), while achieving 3.7 mAP and 5.6 mloU improvements.
Table 5: Perffinnyyeeefoommnee trade-off of different low-light vision methods.
Non-architectural Methods
End-toEad Trained Plug-and- Play Module
Canegory
Metric
KinD 73
2200
SMG66)
MAET(I
DENet
FearEHHonser 20
YOLA
FRBNet
Params
8.2M
79K
17.9M
40M
40K
138K
8K
9K
Efficiency
PlopsrCi)
50.6
61.7
79.5
55.0
53.1
FPS(img/s)T
95.8
83.8
33.1
81.1
89.5
Performance
Det(mAP)
69.4
71.1
68.5
72.5
71.3
71.2
72.7
74,9
Seg(mloU)
48.1
48.7
49.7
52.2
56.0
38.7
61.6
5.4 Visualization
The visualization experiment results and feature maps on ExDark are presented in Fig. 3 The top line in Fig. ([a) verifies FRBNet achieves the most accurate detection. In Fig. 3(b), FeatLnHancer brings color deviation artifacts, and YOLA struggles with fine details in low-light regions. FRBNet generates more balanced feature representations with better preservation of object boundaries and structural details, especially in the outlines. From the heatmaps Fig. C) compared to the Baseline, method produces contours such as the bicycle frame and the human head, which allows FRBNet to preserve fine object details and reveal richer gradient variations. Our approach successfully isolates illumination-invarin features, thereby enhancing robustness for downstream tasks.
spatially focused feature responses, particularly around object
FRBNet(Ours)
Baseline
FeatEnlluncer
YOLA
FRBNet(Ours)
Ground Truth
(a) Visualization of comparative results for dark object detection and nighttime semantic segmentation.
Input
FeatEnllancer
YOLA
FRBNet(Ours)
(b) Visualization of output features from different plug-and-play modules.
Input
Raseline
FRBNet backbone
Baseline, FRBNEE
(c) Visualization of feature maps at different stages of downstream fasks with or w/o trained FRBNet.
Figure 3: Qualitative results. (a) Visualization of comparative results for dark object detection on
Ex (top) and nighttime semantic segmentation ACDC-Night (bottom). (b/iialliio of output features from different plug-and-play modules. (c) Visualization of feature maps at different
stages of downstream tasks with or without trained FRBNet.

---

6 Conclusion
This paper presents FRBNet, novel frequency-domate framework for extracting illumination- invariant features in low-light conditions by leveraging learnable radial basis filters with frequency- channel operations. This plug-and-play module can be seamlessly integrated into existing archi- tectures and achieves significant performance improvements. Based on extensive experimental demonstrations, FRBNet can effectively address the limitations of spatial-domain approaches for low-light downstream tasks. Future research will focus optimizing the universality of modules and exploring broader application scenarios to further advance the development of low-light vision.
Acknowledgments and Disclosure of Funding
We would like to thank the anonymous reviewers for their valuable suggestions and comments. This work was supported by the National Natural Science Foundation of China (NSFC) under grant 602662206307 and 12401590.
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A Technical Appendices and Supplementary Material
A.1 Revisiting Imaging Principle of Low-light Vision
Our extension of the Lambertian model draws significant inspiration from the Phong model's ss additive component approach. The Phong lighting model [45] provides comprehensive framework for simulating light-surface interactions through additive component decomposition. This model serves as the theoretical foundation for our extended Lambertian formulation in the main text, particularly in our treatment of non-uniform highlights in low-light imagery.
The cornerstone of the Phong model its decomposition of surface illumination into three distinet additive components:
= Ia La Is:
(21)
where, la represents ambient reflection component, La represents diffuse reflection component, and Lg represents specular reflection component.
+
Ambieat
Dilluse
Specular
Final
Figure 4: Illustration of Phong Lighting Model Imaging Mechanism
This additive decomposition directly inspired our approach in Eq. of the main text, where extended the traditional Lambertian model by adding a spatially irregular highlight component Sc y).
The standard Phong model was originally developed for controlled lighting environments computer graphics rendering. Our approach extends this concept address the unique challenges of real-world low-light imagery. While the Phong model assumes idealized light sources and surface properties, actual low-light scenes contain complex lighting elements like streetlights, vehicle headlights, and neon signs that create irregular highlight patterns.
To account for these complexities, adapt the Phong model's additive principle by introducing a spatially varying highlight term Hc( y) that modulates the base diffuse reflection. This modi- fication preserves the fundamental additive relationship between components while providing the flexibility needed to represent the non non-unifonn uniform highlight distributions characteristic of natural low- light environments. By building upon the Phong model's 's decomposition approach rather than strictly adhering to its original formulation, our extended Lambertian model can more accurately represent the complex illumination patterns found in real-world low-light imagery.
Figure illustrates the Phong model's imaging mechanism, visually demonstrating how the three components (ambient, diffuuee and specular) combine additively to create the final rendered image. This decomposition serves as the theoretical foundation for our treatment of complex lighting in low-light scenes described in the main text.
A.2 Derivation of Frequency Correlation Coefficient
The characterization of inter-channel relationships the frequency domain is crucial for understand- ing how highlight interference manifests across different color channels. While our previous analysis identified the presence of chase-modulatee residual components, we need a precise mathematical formulation to quantify this directional phenomenon. To this end, we introduce the frequency cor- relation coefficient Cor RG that captures the angular displacement between channel responses. The following derivation formalizes this relationship in the frequency domain, providing the mathematical basis for our angular-mood modulated filtering approach. Given an image Ic y), where E R, G, B denotes the color channel, define its wwo-dimensional Discrete Fourier Transform (DFT) as:
1 ΣΣ
Ic(u, v) Fllc(z, v)] wh r=0
Io(a, w) 12 (2+))
(22)
22

---

where h and w denote the height and width of the image. and (u, v) represents the complex Fourier coefficient frequency component (u, v). Each Fourier coefficient encodes both magnitude and phase information, which can be separated using the complex exponential representation:
(u, v) (u, ()))))
(23)
where (I, u denotes the magnitude and &e (u, v) represents the phase at frequency (u, v). To
further characterize the structural consistency between color channels in the frequency domain, follow the derivation of complex correlation coefficients based on amplitude and phase analysis [56].
For clarity, let denote the magnitude as ac [Zc(u, v) and the phase pC = Φo(u, v) for each channel C. We can then express the Fourier coefficient as:
[u u) ac cos(pc) + iac sin(pc) ac etcc
(24)
The magnitude of this complex coefficient can be verified as:
[I, w)] V / + sin? (pc)) = ac
(25)
For two frequency responses at the same spatial frequency (u. from distinct channels, e.g.. In(u, v) = ageeR and (u, age'ra, we define the complex correlation coefficient as:
In(u, v) Io(u, v) [In(u, [)] Io(u, r)]
Corna
(26)
where Io(u, v) is the complex conjugate of If (u, v), computed as (u, v) = age age ipc, Expanding this equation:
a Re age ape ipp
Carnalu, v)
(27)
on "PRGee"PR-PPr
(28)
(G)
(29)
-
(30)
where Ap= PR Pc represents the phase difference between the R and G channels at frequency (u, v). This elegant formulation reveals a fundamental insight: the correlation between channels at each frequency location is directly encoded by their phase difference Ap. The correlation coefficient Corna (u, v) has unit magnitude but carries critical directional information:
When Ap = 0, Cor (u, v) = 1, it indicates perfect phase alignment between channels.
When Ap = म, Corno v) = 1, reveals exactly opposite phases.
When Ap 3. Cor (u, v) +-, it corresponds orthogonal phase relationships.
The correlation coefficient can also be expressed in terms of its real and imaginary components:
Cor v) cos(Ap) + sin)
(31)
This phase-based correlation measure provides crucial insights into the directional patterns of highlight interference across color channels. In our extended Lambertian model with highlight interference, these phase differences encode the angular displacement of interference patterns, which cannot be captured by simple magnitude-bass based analysis. By incorporating this correlation coefficient into our frequeney- -oomii filter design, enable directionally processing that adapts to the specific phase relationships induced by complex lighting conditions. This theoretical foundation directly informs our ingular-modlaan filtering approach, allowing to effectively isolate illumination- invariant features even the presence of highly directional highlight interference.
A.3 Implementation Details
Statistics of the Datasets Table summarizes the statistics of our employed datasets. These datasets cover wide range of low-light vision tasks, including object detection, face detection, semantic segmentation, and instance segmentation. ExDark[40) is one of the most widely used benchmarks for
23

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dark object detection, featuring diverse scenes and object categories under extremely low-light condi- tions. Dark Face [73] focuses on the challenging task of face detection in dark environments, providing densely annotated facial regions. ACDC-Night
targets nightrime semantic segmentation, with particular emphasis on road scenes, making it valuable for autonomous driving applications. L.IS is recently proposed dataset designed for low-light instance segmentation, offering fine-grained annotations in real-world dark scenarios. The combination these datasets enables a comprchensive evaluation across different low-light vision tasks. # Class is the number of classes, whereas #Train and #Val denote the number of training and validation samples for each dataset, respectively.
Table Statistics of the datasets
Dataset
Task
Class
Train
Val
ExDark
Dark object detection
3000
1800
Face
ADCD Night
Nightline segmentation
LIS
Low-light instance sogmentation
1561
669
FRBNet on Dark Object Detection. For all experiments, adopt the official implementations of YOLOv3 and TOOD detectors with standardized training protocols. The YOLOy3 detector uses a Darknet-53 backbone pre-trained on ImageNet, while TOOD employs ResNet-50 backbone with FPN. Both models are trained for 24 epochs using the SGD optimizer with momentum 0.9 and weight decay 5e-4. The learning rate begins 0.001 with linear warm-up for the first 1000 iterations. We apply standard data augmentation techniques, including random expansion, minimum IoU random cropping, random resizing, random flipping, and photometric distortion. For testing, images resized to 608x608 maintaining the aspect ratio. We use a batch size of 8 on a single GPU.
Baseline
DarkIR
FeatEnHancer
YOLA
FRBNet(Ours)
Figure 5: Qualitative comparisons of dark object detection methods on ExDark dataset.
Table and 6 present comprehensive quantitative comparisons on the ExDark dataset using YOLOv3 and TOOD detectors, respectively. We evaluate detection performance across all object categories, reporting both snlegory-specifie Average Precision (AP) and overall mean Average Precision (mAPso).
FRBNet demonstrates particularly strong improvements on challenging categories such as "Bottle" (+2.6% over YOLA with YOLOv3), "Bus" (+1.8% over DAINet with YOLOv3), and "Chair" (+2.3% over DAINet with YOLOv3). These categories typically involve smaller objects or objects with challenging contrast profiles in low-light conditions, suggesting that effectively preserves discriminative features for these difficult cases. Figure presents qualitative comparisons of detection results from various methods on challenging Ex samples. As shown in the visualization, our FRBNet achieves more accurate object localization and higher detection confidence compared to the baseline, DarkIR, FeatEnHancer, and YOLA methods.
beqquency-domale processing
FRBNet on Dark Face Detection. We continue with YOLOv3 and TOOD as base detectors, largely following the experimental setup from YOLA(5) Since the UG2+ Challenge concluded in 2024, we adopted standard random split with a 3:1:1 ratio for training, validation, and testing. Our implementation is based on the MMDetection framework with customized data pipelines. For

---

Table 7: Quantitative comparisons of the ExDark [40] dataset based on YOLOv3 detector.
Method
Bicycle
Buat
Botile
Bus
Car
Cal
Chair
Cup
Dog
Motorbike
People
Table
mAApo
Baseline
64.4
67.6
77.7
MBLLEN
77.5
72.5
70.2
80.7
65.0
65.2
70.6
64.9
77.3
41.8
70.3
KIND 15
80.2
74.4
71.5
81.0
80.3
62.2
61.3
67.5
75.8
75.9
40.9
69.4
Zero 7er.-DCT DCT 20
81.8
74.6
70.1
86.3
79.5
61.0
66.2
71.7
78.4
62.9
77.3
43.1
71.1
EalightenGAN 26
81.1
74.2
69.8
83.3
78.3
63.3
65.5
69.3
75.3
62.5
76.7
41.0
70.0
RUAS 36
76.4
69.2
62.7
77.3
74.9
59.0
64.3
64.8
73.1
55.8
71.5
38,8
65.7
SCT +5
80.3
74,2
73.6
82.8
78.4
64.4
65.8
71.3
78.1
62.7
78.2
42.4
71.0
NcRCo F0
80.8
73.6
66.3
81.3
75.6
62.8
62.5
67.7
75.6
61.8
75.1
39.0
68.5
SMG 60
78.1
72.1
65.8
81.6
78.3
63.7
64.5
67.6
76.3
57.4
73.7
42.4
68.5
LighDDiff
81.7
74.1
73.3
85.2
80.2
62.5
67.3
71.4
74.7
63.5
75.8
46.1
71.3
DarkiR
78.5
73.3
84.9
73.7
DENe
76.7
76.5
71.9
84.2
59.9
64.6
66.7
73.9
MAET 10
81.5
73.7
74.0
88.2
80.9
68.8
66.9
71.8
79.3
60.2
78.8
46.3
72.5
FemtEn|
79.7
75.9
73.3
87.5
81.2
62.0
64.9
67.9
75.7
64.2
76.6
45.3
71.2
DAINet [
81.1
77.7
74.1
89.4
80.4
68.6
09.3
71.1
81.5
63.3
78.6
45.1
73.5
YOLA
S
82.4
74.0
72.7
85.4
81.0
67.2
66.5
71.5
81.8
65.2
78.6
45.7
72.7
FRBNettour)
84,3
75.6
75.3
89.8
82.0
68.6
71.6
74,8
82.6
65.8
81.0
46.5
74,9
Table Quantitative comparisons of the ExDark [40] dataset based on TOOD detector.
Method
Bicycle
Boat
Bottle
Bus
Car
Cat
Chair
Cup
Dog
Motorbike
People
Table
mAP 50
Baseline
[6
80.6
75.8
71.1
88.1
76.8
70.4
66.8
69.2
85.4
61.5
76.1
48.2
72.5
MBLLEN 41
80.8
77.8
72.8
89.3
78.7
73.5
67.5
69.4
85.2
62.9
77.3
47.2
73.5
KIND 76
81.7
77.7
70.3
88.4
78.1
69.7
67.2
67.8
84.1
61.6
76.6
47.8
72.6
7cm-DCF
81.8
79.0
72.9
89.6
77.9
71.9
68.5
69.8
84.8
62.9
78.0
49.5
73.9
EalightenGANN
70.6
68.7
RUAS 36
78.4
74.3
67.4
72.4
67.7
65.2
73.4
47.0
81.3
89.4
77.6
71.1
68.0
70.9
63.0
77.2
49.2
73.5
NeRCo
70
78.8
75.6
70.8
87.6
75.7
69.1
66.8
69.5
82.5
59.9
76.0
49.3
71.8
SMG 66
78.2
75.9
69.9
87.3
75.1
71.3
66.5
67.2
84.2
60.1
75.1
46.7
71.5
LightDif
26
81.1
77.8
74.4
89.5
79.2
72.0
67.6
70.9
86.1
62.5
77.2
49.0
72.9
DarkR
15
78.4
78.0
70.4
88.7
76.0
70.8
67.9
66.5
83.7
39.5
75.2
49.0
72.0
DENet
4M
80.9
78.2
70.9
88.3
77.5
71.6
67.2
70.3
87.3
62.0
77.3
49.9
73.5
PENet
74
76.0
72.3
66.7
84.4
72.2
65.4
63.3
65.8
79.1
53.1
71.0
44.6
67.8
MAET
10
80.5
77.3
74.0
90.1
78.3
73.4
68.6
70.7
86.6
64.4
77.6
48.5
74.3
FanterHnncer211
83.6
77.4
89.6
72.6
68.2
72.5
63.8
78.0
49.6
74.6
YOLA
83.9
78.7
88.8
73.4
71.9
78.3
49.8
75.2
FRBNet(our)
83.2
78.9
76.5
91.2
80.7
74.1
69.9
72.6
84.6
64.6
78.6
49.8
75.4
training, we apply data augmentation including random expansion (ratio range 1-2), minimum IoU random cropping (IoU thresholds from 0.4 to 0.9), random resizing between (750x500) and (1500x1000) with preserved aspect ratio, and random horizontal flipping with 0.5 probability. During testing, images resized to 1500x 1000 while maintaining aspect ratio. We train YOLOv3 for 20 epochs and TOOD for 12 epochs using the SGD optimizer. Since DarkFace contains only face annotations, configure the detectors for single-class detection.
Figure presents qualitative comparisons face detection results on challenging DarkFace samples. As shown, enhancement- based methods like LightDiffusion and DarkIR improve image visibility but often introduce artifacts or wver-enhancement that can lead to false positives. FeatEnHancer, YOLA, and our FRBNet all maintain the original low-light appearance while accurately detecting faces. Notably, our method achieves more precise bounding box localization and higher detection confidence scores, particularly for faces in extremely dark regions.
FRBNet Nighttime Semantic Segmentation. We further evaluate our approach on nighttime semantic segmentation using the ACDC-Night dataset to demonstrate the versatility of FRBNet across different low-light vision tasks. For semantic segmentation experiments, we adopt the MMSeg- mentation framework with DeepL.bbV33 architecture, employing ResNet-50 backbone initialized with ImageNet pre-trained weights. This configuration allows for direct comparison with previous state-of of- the-art methods nighttime segmentation. During training, images resized to 2048x1024
25

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Fra
Jace 46
Face 39
320
Face 53
Lightiirssoon
DarkIR
FaatEnHancer
Face 35.
Iace
Face
Face 32. Face 34
Fnce
Race
YOLA
FRENA(OUUS))
GT
WULSTON
HIlI
Face
FRace 66.7
Ligusiiifssooo
DarkIR
Hanceer
HIL
HALLE
HIl
Face
YOLA
FRENE(OUUrS)
GT
Figure 6: Qualitative comparisons of dark face detection methods on DarkFace dataset.
resolution, and we use a batch size of 4. The network is optimized using SGD with base learning rate of 0.01 and weight decay of 0.0005, following 20K iteration training schedule.
Input
Baseline
Hantee
YOLA
FREN&
GT
Figure 7: Qualitative comparisons of semantic segmentation methods on ACDC-Night dataset.
Figure presents qualitative comparisons of segmentation results on the ACDC-Night dataset. The visualization reveals significant differences in segmentation quality across methods. The baseline method struggles with class boundaries low-light conditions, producing fragmented and incon- sistent segments, particularly visible in the second and third rows. FeatEnHancer improves overall
26

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Baseline
DarkIR
FaalEnhkunor
YOLA
FRENd(OUES)
GT
Figure 8: Qualitative comparisons of instance segmentation methods on LIS dataset.
segmentation but still misclassifies certain regions, especially in areas with strong light sources or deep shadows. YOLA produces more coherent results but exhibits some boundary inaccuracies and class confusion in complex scenes. In contrast, our FRBNet generates segmentation maps that more closely align with ground truth, maintaining consistent class boundaries even extremely dark regions. This is especially evident in challenging scenarios like road boundaries under street lighting, distant buildings in minimal ambient light, and complex urban scenes with mixed lighting sources.
FRBNet on Low-light Instance Segmentation. To further evaluate the versatility of our approach across wider range of low-light vision tasks, we conduct experiments on the Low-light Instance Segmentation dataset, which requires both object detection and instance-level segmentation in challenging illumination conditions. We employ Mask R-CNN with ResNet-50 backbone as our base architecture, implemented using the MMDetection on framework. During training, images resized to 1333x800 while maintaining aspect ratio, and standard random horizontal flipping applied with a probability of 0.5. We train all models with batch size of 8 using SGD optimizer with an initial learning rate of 0.01, momentum of 0.9, and weight decay 0.0001. The learning rate schedule consists of linear warm-up phase for the first 1000 iterations. followed by multi-step step decay, reducing the learning rate by a factor 0.1 epoch 18. All models are trained for 24 epochs with mixed precision training enabled.
Table Quantitative comparisons of the Low-light Instance Segmentation 4 dataset based on Mask RCNN.
Method
mApeeg
AP
AP
mApbos
APloz
APhaz
Baseline
22
23.7
41.5
23.3
29.2
52.9
29.3
MBLLEN
22.5
40.7
22.3
28.5
52.0
28.4
Zero-DCE 20
25.1
44.5
24.6
30.3
55.3
29.4
DENet 46
16.1
31.0
15.4
19.5
40.0
15.7
DarkIR
27.4
46.3
27.5
32.7
56.7
34.4
YOLA (
24.9
44.8
24.2
30.7
56.4
29.3
FeatEnHancer 21
29.1
48.7
29.7
34.0
57.6
35.3
FRBNet(Ours)
30.2
50.5
30.4
36.9
61.2
38.4
Beyond the segmentation results already analyzed in the main text, this dataset also contains bounding box annotations for detection. Therefore, we conducted additional experiments and found: FRBNet shows even larger gains, achieving 36.9% Aphoz compared to 34.0% for FeatEnHancer and 32.7% for DarkIR. This indicates 2.9-point improvement over the previous state-of-the-art. Notably, our approach demonstrates the most substantial improvement at Ap (38.4% vs. 35.3%), which requires more precise localization, highlighting the effectiveness of our frequency-domain features for accurate object boundary delineation.
27

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A.4 Extended Experiments
To further demonstrate the flexibility and task-level generalization of our frequency-domall feature enhancer, we additionally conducted experiments two more tasks:
FRBNet Low-light Image Classification. We adopt the official implementation of the low- light image classifier, using ResNet-101 as the backbone and following the standardized training protocols prescribed for the [30 dataset. For comparison, we include the Baseline model, FeatEnhancer [21]. and YOLA [S] as representative prior approaches. The reported accuracies in Table[odemonsst
that our method consistently outperforms all competitors, achieving the highest classification accuracy among the evaluated methods. This performance gain can be attributed to our frequency-donen domain design, which effectively preserves diseriminative cues under challenging low-light conditions.
FRBNet Low-light Video Action Recognition. Furthermore, conduct experiments low- light video action recognition using the ARID dataset [67), implemented within the MMAction2 framework and employing the TSN [61] architecture with ResNet-50 backbone. We evaluate model performance using both Top-1 and Top-5 accuracy metrics, as reported in TablcT]] our method achieves substantial improvements over both the Baseline and YOLA 5 registering the highest scores across all metrics.
Table 10: Quantitative comparisons of the Low-
Table 11: Quantitative comparisons of the Low-
light Image Classification.
light Video Action Recognition.
Method
Acc
Method
Top-1
Top-5
Baseline
155
86.8
Baseline
42.65
96.27
FeatEnHancer
21
82.0
YOLA 5
41.09
93.68
YOLA
5
86.4
FRBNet 88.2
FRBNet(Ours)
44.84
96.53
To further examine the effectiveness of our proposed FRBNet, we conducted additional ablation experiments by replacing the learnable frequency-domain filter with standard convolutional layers. Specifically, we implemented 3x3, 5x5, and 7x7 convolution kernels and evaluated these variants on the ExDark and DarkFace datasets using YOLOv3, As reported in the Table 12] our method consistently and significantly outperforms all convolution- bbased counterparts across different kernel sizes. This observation aligns with our original motivation: while standard convolutions exhibit spatial shift-invariance they are inherently limited in capturing structured dependencies within the frequency domain. In contrast, our approach employs radial basis function (RBF) network construct spectrally selective and directionally modulated filter, enabling the preservation and enhancement of localized frequency cues under real-world low-light conditions. Such spectral- is particularly crucial for effectively handling the non-uniform illumination of low-light imagery.
adaptability
Table 12: Additional ablation experiments on FCR with convolution layers of varying kernel sizes.
ExDark
DarkFace
Method
Recall
mAP
Recall
mAP
Our
90.6
74.9
75.7
57.7
Conv 3x3
84.6
72.7
72.4
55.3
Conv 5x5
85.4
73.0
73.7
54.8
Conv 7x7
85.3
72.3
72.6
54.6
A.5 Discussion
Limitations. Although FRBNet has demonstrated impressive performance various low-light visual tasks, there still exist limitations. Due to the current design of FRBNet mainly addresses image degradation issues related to illumination, FRBNet may show less effective performance in low-light scenes with more complex degradation conditions, such motion blur. This problem may be solved
28

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by introducing all-in-one modules or forming models that consider more potential types of image degradation, which is direction of our future work.
Broader Impacts. Our work contributes to enhancing visual perception systems in low-light environ- ments, with potential applications in safety-critical domains like autonomous driving, surveillance, and emergency response. However, improved low-light vision perception also raises privacy concerns, it might enable surveillance in previously invisible lighting conditions. We encourage responsible deployment of these technologies with appropriate privacy safeguards and regulatory compliance.
29


Reviews:
[{"review_id": "zOEtzPVZV6", "reviewer": "Reviewer_s1m7", "summary": "The authors present a novel frequency-domain framework for extracting illumination invariant features in dark images by learning radial basis filters. This plug-and-play module can be seamlessly integrated into existing low-light vision networks and achieves performance improvements.", "questions": "The same as in the weakness points.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths \nThe approach is not a totally data-driven technique and has a physical ground instead.\nThe PnP strategy provides adaptability.\nThe efficiency and performance are good.\n\nWeaknesses\n1. The overexposure and underexposure happen frequently in nighttime videos and violate the physical model. will this hamper the performance? why?\n2. The relation to existing frequency-domain related work?\n3. The segmentation performance on some classes is not that advantageous (some columns in Tab. 2), why? \n4. I expect some failure examples and analysis.\n5. The visualized features in Fig. 3(c) do not show advantages, those in the backbone look similar (signs might be opposite), and the heatmaps are not better (higher attention on the head is reasonable, but on corners make no sense).\n6. The title is too big and I suggest focusing on the proposed technique.", "was_revised": false, "final_justification": "", "rebuttal": "**Thank you for your constructive feedback and for recognizing the strengths of our work.** Below are detailed responses to your concerns:\n> ***W1: The overexposure and underexposure happen frequently in nighttime videos and violate the physical model. will this hamper the performance? why?***\n\nA1: Thank you for raising this important concern. Overexposure (pixel saturation leading to loss of detail) and underexposure are common challenges in low-light scenes. However, our approach does not aim to reconstruct raw pixels. We focus on extracting robust illumination-invariant features to support downstream tasks under real-world low-light conditions. To better model challenging lighting, we extend the classical Lambertian model by introducing a highlight term. Our method is designed to suppress interference and indirectly reduce the impact of overexposed regions on feature representation from the frequency domain. Importantly, our model is not intended as a precise law for all conditions, but rather as a generalized approximation that guides network design. Compared to purely data-driven methods, this physical grounding improves interpretability and robustness. \n\nTo further validate this, we simulate overexposure using ExDark[1] data under “Single”, “Weak”, and “Screen” lighting conditions. We apply a gamma-based nonlinear transformation to create partially overexposed images. Then we evaluate our method, baseline models, and YOLA(NeurIPS24)[2] on these transformed images. This suggests that overexposure doesn’t significantly hamper the performance of our method.\n\n| Dataset | Method | Recall | mAP |\n| - | --- | --- | --- |\n| |Baseline | 88.7 | 78.7|\n| Exdark_var. | YOLA (*NeurIPS2024*)| 89.3 | 79.9 |\n| | Our Method| **91.2** | **80.8** |\n\n**Table 1:** Quantitative comparisons based on YOLOv3 detector using ExDark_var.(Overexposure)\n\n---\n> ***W2: The relation to existing frequency-domain related work?***\n\nA2: We appreciate the opportunity to clarify this point. As discussed in Section 5.2, most existing frequency-domain methods primarily focus on image enhancement via spectral decomposition, typically modifying low-frequency illumination components while preserving high-frequency details[3,4,5]. In contrast, as **Reviewer tvFq** pointed out, our work is the first to adopt the channel ratio for illumination-invariant features in the frequency domain. This is a shift from pixel-level enhancement to feature-level learning. Furthermore, our angular-modulated radial basis filtering uniquely adapts to phase-structured interference, which is unexplored in prior works. We will revise the *Related Work* section to better analyze the relation to existing frequency-domain related work.\n\n---\n> ***W3: The segmentation performance on some classes is not that advantageous (some columns in Tab. 2), why?***\n\nA3: Thank you for the detailed observation. While current results reveal category-specific challenges, the optimization objective of our method focuses on illumination invariant filtering to facilitate low-light downstream tasks universally. In semantic segmentation tasks, class imbalance and intra-class variance will lead to a decrease in segmentation accuracy for individual categories. For example, *terrain* and *pole* account for only 0.81% and 1.01% of total labels. Some classes (such as *terrain vs. vegetation*) exhibit highly similar textures, color cues, and blurred structural boundaries, especially under limited illumination. These factors undermine the discriminative features required for accurate segmentation, making such distinctions inherently difficult for all methods. Crucially, our method still delivers significant overall gains for low-light semantic segmentation (in Tab.2), addressing the core goal of low-light vision tasks. Furthermore, our method’s plug-and-play characteristic enables seamless integration with category-sensitive mechanisms (e.g., re-weighting losses), which will be a scalable and sustainable research direction in future work.\n\n---\n> ***W4: I expect some failure examples and analysis.***\n\nA4: We agree that including failure cases and their detailed analysis would provide a more complete understanding of our work. Due to page limitations, we discussed the limitations of our method in Appendix A.4. As an example, in the last image of Fig. 7 (bottom row), our method shows imprecise segmentation on the lower right corner (confusing the fine contours of the tree markings with vegetation). This is likely due to motion blur in the input image caused by vehicle movement under low light, which suppresses high-frequency structural cues. While our method improves overall performance, such blur-related degradation remains a challenge. \nWe will incorporate concrete failure examples and a more thorough analysis in the Appendix of the revised version.\n\n---\n> ***W5:The visualized features in Fig. 3(c) do not show advantages, those in the backbone look similar (signs might be opposite), and the heatmaps are not better (higher attention on the head is reasonable, but on corners make no sense).***\n\nA5: This is an impactful review for improving our paper!  We will revise the paper to provide more details in Fig.3.\nThe visualized feature map of “Baseline_backbone” is from the first layer of backbone with the input of the raw image, while the visualized feature map of “FRBNet_backbone” is from the same layer of the same backbone with the input processed by our method.\nTheir overall structure may look similar because they are from the same backbone with the same input image. Our method enhances object details and reveals richer gradients, while the Baseline feature appears flatter and lacks spatial focus.\n\nThe heatmaps are from the first layer’s output features of the neck part. Apart from corners, our method yields sharper responses around meaningful regions, such as the contours of the bicycle and the human head. More importantly, our method achieves the accurate detection of this image, while the baseline does not. The enhanced attention on the key semantic region supports the effectiveness of our method in improving low-light downstream tasks' performance in detection and segmentation.\nWe will replace the visualization with a clearer version and include more examples in the Tech. Appendix.\n\n---\n> ***W6:The title is too big and I suggest focusing on the proposed technique***\n\nA6: Thank you for the helpful suggestion. We will revise the title to: “FCRFilter: Learning Provable Illumination Invariance for Low-light Vision via Frequency-domain Channel Ratio Filtering”. Correspondingly, we will also revise the related terminology and abbreviations throughout the paper to maintain consistency.\n\n***\n**Reference**\n> [1] Yuen Peng Loh et al. Getting to know low-light images with the Exclusively Dark dataset. CVIU 2019. \n\n> [2] Mingbo Hong et al. You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection. NeurIPS 2024. \n\n> [3] Wenbin Zou et al. Joint wavelet sub-bands guided network for single image super-resolution. IEEE Transactions on Multimedia 2022.  \n\n> [4] Haohan Wang et al. High-frequency component helps explain the generalization of convolutional neural networks. CVPR 2020. \n\n> [5]  Chenxi Wang et al. Fourllie: Boosting low-light image enhancement by fourier frequency information. MM 2023."}, {"review_id": "e5KsQovd2b", "reviewer": "Reviewer_2mko", "summary": "This paper proposes an image processing module for computer vision tasks under low-light conditions. The module can be trained using only downstream task losses (plug-and-play).\n\nThe authors extend the traditional Lambertian model (which only considers diffusion components) by adding local light source terms from the Phong model. By converting color ratios to log scale and transforming them to frequency space, they demonstrate that highlight components can be linearly separated.\n\nIn the proposed method, they apply a learnable frequency domain filter (LFF) to the Fourier-transformed log-scaled color ratios. After applying LFF and performing inverse Fourier transformation, they concatenate the resulting log-scale color ratios and process them with convolution. Similarly, they apply convolution to the original image, concatenate both the processed color ratio and original image, and apply another convolution before feeding the result into CV tasks.\nThey show the method improve multiple computer vision tasks under low light conditions.", "questions": "1. Since $F_{inv}^{C_i C_j}$ corresponds to $log(I_{C_i}/I_{C_j})$, is there a specific reason for concatenating with the original image I rather than transforming back via exp? What is the reason for concatenating instead?\n\n2. Is the number of channels in $F_{out}$ equal to 3? If so, is the result image displaying $F_{out}$? There doesn't seem to be any visible difference from the GT image.\n\n3. How were the predefined $\\mu_k$ values determined?\n\n4. Would it be possible to conduct an ablation study using only FCR with Convolution layers?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:  \nOriginality:  \n- Proposal of a novel approach:\n  - Extension of the Lambertian model with the Phong model, implementing filtering in frequency space\n  - Learning method using downstream task losses (plug and play)\n  - Application of knowledge about separating local light sources to preprocessing for CV tasks\n\nWeaknesses:  \nQuality:  \n- Theoretical foundation:\n  - Unclear validity of the assumption that $H_C$ is very small compared to ambient light. It seems to have much higher irradiance than diffuse components.\n  - The derivation process of equation (8) is not explicitly shown, and the rationale for assuming color channels have intensity only at specific frequencies is unclear\n\nClarity:\n- Explanation of the method is unclear and difficult to understand:\n  - Please add an explanation of the Radial basis function, particularly how the predefined $\\mu_k$ values were determined\n  - The number of channels in $F_{out}$ and the method of generating the final output (details of Convolution layer) are not clear\n- Weak connection between theoretical background and proposed method:\n  - Insufficient explanation of how filtering in frequency space solves the local light source problem\n  - The relationship between equations (8)-(10) and LLF (spatial Gaussian, Gaussian weights for values, weights in frequency space) is not clear\n\nSignificance:\n- This method requires to prepare low light data and labels for each task for training\n\nMinor comment:  \nIn Section 2, the statement \"As shown in Eq. (5), the nonlinear residual from the highlight term disrupts the clean separation of illumination and reflectance, limiting the effectiveness of spatial-domain channel ratio methods\" - shouldn't this be \"separation of highlight and diffuse\" rather than \"separation of illumination and reflectance\"?", "was_revised": true, "final_justification": "The rebuttal explanations address my main concerns, and the authors have agreed to implement the requested revisions. \nI have therefore updated my recommendation accordingly.", "rebuttal": "**Thank you for your constructive assessments of our work,** and we address the concerns point by point as follows:\n\n> ***W1: Unclear validity of the assumption that $H_C$ is very small compared to ambient light. It seems to have much higher irradiance than diffuse components.***\n\nA1: Thank you for this comment. We respectfully clarify that we **don’t assume** $H_C$ is very small compared to ambient light in terms of irradiance. $H_C$ is not absolute irradiance, but a coefficient. As stated in Section 2.1, we define $H_C$ as the relative intensity between $D_C$ (the spatially irregular highlight componet) and $S_C$ (the standard diffuse reflection componet). The relationship of them is  $ S_C (x,y)=H_C (x,y) \\cdot D_C (x,y) $ and $H_C (x,y)∈[0,1)$. We will further clarify this to avoid potential misinterpretation in the revised version.\n\n---\n> ***W2: The derivation process of equation (8) is not explicitly shown, and the rationale for assuming color channels have intensity only at specific frequencies is unclear.***\n\nA2: Thank you for your insights into the theoretical foundation. We would like to clarify that equation (8) is a standard polar-form representation in complex space, where each frequency component can be expressed as an amplitude-phase pair:\n$\\mathcal{H}=a \\cdot e^{i \\theta}$. This is commonly used in frequency-domain analysis, and we have provided a more detailed explanation in Appendix A.2 (Eqs. 22-24). \n\nWe also would like to clarify that we **don’t assume** color channels have intensity only at specific frequencies. Our analysis is conducted across the entire frequency spectrum, not limited to specific bands. We will consider your concerns and make modifications to the manuscript to make it clearer.\n\n---\n> ***W3&Q3: Please add an explanation of the Radial basis function, particularly how the predefined $\\mu_{k}$ values were determined. & How were the predefined $\\mu_{k}$ values determined?***\n\nA3: Thank you for your suggestion. We will include a more detailed explanation of the radial basis function in the Tech. Appendix. We employ nonlinear Gaussian radial basis functions to construct a learnable filter that captures responses at different frequencies.\n\nThe predefined $\\mu_{k}$∈[0,1] values are determined according to the well-established convention in Gaussian kernel radial basis function networks to represent normalized radial frequency radii which are selected to be evenly spaced across this range. \n\n---\n> ***W4&Q2: The number of channels in $F_{out}$ and the method of generating the final output (details of Convolution layer) are not clear. & Is the number of channels in $F_{out}$ equal to 3?***\n\nA4: Thank you for your question. As illustrated in Figure 2 (bottom-left corner), the number of channels in $F_{out}$ is 3. The details of the convolution layer (structure, channel dimensions, etc.) are also provided there. We apologize for the inconvenience caused by the small font in Figure 2 and will optimize the display of this part in the revised version. In addition, we will explicitly describle the convolutional structure to avoid ambiguity. More comprehensive implementation details are also available in our released code.\n\n---\n> ***Q2: If so, is the result image displaying $F_{out}$? There doesn't seem to be any visible difference from the GT image.***\n\nA4(2): We would like to clarify the distinction between various result images. Figure 3(a) presents **the final results of downstream tasks**(e.g. bbox and mask) after processing by the entire pipeline. Compared to other methods, our results exhibit clearer boundaries and more accurate bounding boxes, which are closer to the ground truth. Figure 3(b) presents the visualization of extracted invariant features $F_{out}$ from different plug-and-play modules including FeatEnHancer, YOLA, and the proposed method. **The similarity between our result images and the GT images confirms the effectiveness of the proposed method**. We will further clarify the results shown in Figure 3 to avoid misinterpretation.\n\n---\n> ***W5: Insufficient explanation of how filtering in frequency space solves the local light source problem.***\n\nA5: Thank you for highlighting this point. We will expand the explanation in the revised version to clarify how our method addresses the influence of local light sources. Local light source appears as sharp, localized brightness variations in the spatial domain, which correspond to structured high-frequency components in the frequency domain. As established in [1,2], the low-frequency components are often associated with smooth illumination variations and high-frequency components with reflectance edges and details. Our method leverages this property by applying learnable frequency-domain filters to select inconsistent frequency signals. As discussed at the end of Section 2.2:\n\n`we design specialized filtering strategies that aim to robustly extract invariant illumination features thus enhancing the reliability and effectiveness of feature extraction under varying lighting conditions.` \n\nWe will make this more explicit in the revised version.\n\n---\n> ***W6: The relationship between equations (8)-(10) and LLF (spatial Gaussian, Gaussian weights for values, weights in frequency space) is not clear.***\n\nA6: We respectfully clarify that terms such as “LLF”, “spatial Gaussian”, “Gaussian weights for values”, and “weights in frequency space” **do not appear** in our paper, which leads to some confusion on our side. We infer the reviewer may seek a connection between the theoretical equations and our method. This connection is described at the beginning of Section 3, where we state that our method is inspired by extended image formation principles. Specifically, Eqs. (8)–(10) present the derivation of our frequency-domain channel ratio, which forms the theoretical foundation for our frequency-domain radial basis network in Section 3.\nWe will revise the paper to avoid misinterpretation.\n\n> ***W7: This method requires to prepare low light data and labels for each task for training***\n\nA7: Thank you for this significant comment. Our method is designed to improve task performance under realistic illumination constraints in low-light vision by **supervised learning**, where training with labeled low-light data is common and necessary. Our method is implemented as a plug-and-play feature extraction module, which can be integrated into existing networks without changes to the backbone. We appreciate your concern and plan to explore unsupervised extensions in future works.\n\n---\n>***Q1:Since $F_{inv}^{C_i C_j}$ corresponds to $log (I_{c_i} / I_{c_j})$, is there a specific reason for concatenating with the original image I rather than transforming back via exp? What is the reason for concatenating instead?***\n\nA8: We would like to clarify that $F_{inv}^{C_i C_j}$ is **not simply corresponding to** $log (I_{c_i} / I_{c_j})$, but rather the result of nonlinear learnable frequency-domain filtering (LFF) applied to log-ratio inputs. Our goal is not to reconstruct the image, but to extract illumination-invariant representations. The log-ratio is a well-established technique for enhancing structural contrast. Applying exp would simply reverse the transformation, potentially losing the beneficial properties of the log space. We concatenate the illumination-invariant feature with the original image feature to combine complementary information. This fusion enables the backbone to exploit both robustness and expressiveness, and is also supported by findings in related works[3].\n\n---\n> ***Q4: Would it be possible to conduct an ablation study using only FCR with Convolution layers?***\n\nA9: Thank you for the suggestion. We conduct an additional experiment by replacing our learnable frequency-domain filter with standard convolution layers of kernel sizes 3×3, 5×5, and 7×7, forming a baseline of “FCR with Convolution Layers”. The results are reported in Table 1, where our method significantly outperforms all convolution-based variants on both ExDark and DarkFace datasets, particularly in Recall and mAP. \n\n| |ExDark| |DarkFace| |\n|-|-|-|-|-|\n| |Recall|mAP|Recall|mAP \n|Our|**90.6**|**74.9**|**75.7**|**57.7**\n|Conv 3*3|84.6|72.7|72.4|55.3\n|Conv 5*5|85.4|73.0|73.7|54.8\n|Conv 7*7|85.3|72.3|72.6|54.6\n\n**Table 1:**  Ablation study on FCR with convolution layers of varying kernel sizes.\n\nThis result reflects our original design motivation: **standard convolutions are spatially shift-invariant, but they are not well suited for modeling structured dependencies in the frequency domain**. In contrast, our approach leverages a radial basis function network to construct a spectrally selective and directionally modulated filter, which is critical for capturing localized frequency cues under real low-light conditions. We will include this ablation and a more detailed analysis in the revise version.\n\n---\n> ***Response to Minor Comment***\n\nA10: Thank you for the suggestion. In Section 2.1, we have defined the illumination and reflectance components based on our extended image formation model. Since the “diffuse” term is traditionally embedded within the Lambertian formulation (which includes both illumination and reflectance), we chose to describe our objective in terms of isolating illumination-invariant features by separating the illumination and reflectance. Therefore, we believe keeping the current terminology helps maintain consistency and clarity. We appreciate your careful reading and the helpful minor comment :)\n\n---\n**Reference**\n> [1] Haohan Wang et al. High-frequency component helps explain the generalization of convolutional neural networks. CVPR 2020.\n\n> [2] Yang Wang et al. Decoupling-and-aggregating for image exposure correction. CVPR 2023.\n\n> [3] Mingbo Hong et al. You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection. NeurIPS 2024."}, {"review_id": "2brJMBHu7b", "reviewer": "Reviewer_cndh", "summary": "This work proposes a frequency-domain radial basis network for object detection and semantic segmentation under low-light conditions. To address the incomplete modeling of low-light environments in existing methods, the authors extend the Lambertian model with the Phong illumination model to account for non-uniform lighting. They further introduce frequency-domain channel ratios and learnable frequency-domain filters to enhance illumination-invariant features. The proposed method is plug-and-play, and its effectiveness is supported by performance evaluations and algorithmic analysis.", "questions": "1. The authors introduce their plug-and-play solution by reviewing four mainstream strategies for low-light visual tasks, highlighting their own innovation. However, plug-and-play approaches also share a notable limitation: they often lack sufficient interaction with the backbone of downstream perception networks. In essence, these methods still resemble preprocessing approaches, albeit operating in the feature domain. I encourage the authors to explicitly discuss this issue.\n\n2. Given the newly proposed modeling, it remains questionable whether illumination, reflection, and specular components can be effectively separated in the frequency domain. The authors are encouraged to provide further feature-level analysis to strengthen the theoretical justification.\n\n3. Since the proposed modeling is intended to address the incomplete treatment of low-light environments in prior work, it would be beneficial to provide more comprehensive feature-level analysis under highly non-uniform lighting conditions—e.g., nighttime scenes with localized lighting such as street lamps.\n\n4. On the downstream task side, the work only considers detection and segmentation. It would be valuable to extend the range of downstream tasks to better validate the flexibility and generalizability of the proposed method. Additionally, as the method essentially functions as a feature enhancer trained solely under task-specific losses, further analysis is needed to assess its interpretability and whether the claimed benefits are indeed justified.\n\n5. The performance evaluation lacks comparison with representative joint learning methods, such as MAET, IAT, DANNet, and GLASS-GPS. Including such comparisons would provide a more complete assessment of the method’s effectiveness.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The motivation of the proposed method is well-articulated, with a clear presentation of the overall approach and theoretical derivation. The experiments are relatively comprehensive. However, the analysis of existing plug-and-play methods could be further strengthened. One key limitation is that most such methods still focus on feature-level preprocessing, with limited interaction with the backbone of downstream tasks. Additional evidence is needed to support whether components such as illumination, reflection, and highlight residuals can be effectively separated in the frequency domain. The numerical stability of the input preprocessing also requires clarification. Moreover, the experimental section appears to lack comparisons with joint learning approaches.", "was_revised": true, "final_justification": "After carefully reviewing the authors’ rebuttal, I believe they have partially addressed my earlier concerns. Considering this, along with the perspectives of the other reviewers, I have decided to raise my rating to borderline accept.", "rebuttal": "**We sincerely appreciate the reviewer’s constructive feedback.** Below are detailed responses to your questions:\n> ***Q1: The authors introduce their plug-and-play solution by reviewing four mainstream strategies for low-light visual tasks, highlighting their own innovation. However, plug-and-play approaches also share a notable limitation: they often lack sufficient interaction with the backbone of downstream perception networks. In essence, these methods still resemble preprocessing approaches, albeit operating in the feature domain. I encourage the authors to explicitly discuss this issue.***\n\nA1: Thank you for this thoughtful comment. We acknowledge that plug-and-play approaches often face the limitation of limited interaction with downstream backbones. However, as recognized by **Reviewer s1m7**, the PnP strategy provides adaptability, with both efficiency and performance. We also believe its training efficiency and modularity make it highly practical for real-world deployment.\n\nImportantly, our method differs from classical preprocessing approaches. As noted in *Line 129*, image enhancement methods typically optimize for human visual quality but may not guarantee machine perception performance. In contrast, our method is trained end-to-end with downstream task-specific supervision, aiming to extract robust illumination-invariant features that directly benefit downstream tasks, rather than merely improving the visual appearance of images. This strategy preserves the modular advantages and ensures tight alignment with downstream task goals.\n\n---\n> ***Q2: Given the newly proposed modeling, it remains questionable whether illumination, reflection, and specular components can be effectively separated in the frequency domain. The authors are encouraged to provide further feature-level analysis to strengthen the theoretical justification.***\n\nA2: Thank you for raising this point. \nBased on the extended generalized low-light modeling and the theoretical analysis in Section 2, our newly proposed frequency-domain channel ratio (FCR) is composed of illumination, reflection, and high-lit residual (evolved from the specular).\nDue to being converted to the frequency domain, these components exhibit different characteristics due to their respective natures.\n- The illumination component usually exhibits smooth fluctuations within the low-frequency spectrum.\n- The reflection component is usually stable and represents the material and detailed information of the object, contributing to mid-to-high frequency responses. \n- The high-lit residual component, which is derived from the specular term with sharp local peaks and directionally biased energy, usually manifests as localized specific orientations in the frequency domain.\n\nTherefore, **our proposed learnable filter aims to enhance illumination invariant features for low-light recognition by suppressing fluctuating illumination interference and modulating the high-lit residual component, rather than explicitly separating these three components.** In Figure 3, we have already provided feature-level visualizations, including both feature maps and heatmaps, to demonstrate the effective enhancement of our method for illumination-invariant features. Following your suggestion, we will include additional visual and spectral analyses in the Appendix.\n\n---\n> ***Q3: Since the proposed modeling is intended to address the incomplete treatment of low-light environments in prior work, it would be beneficial to provide more comprehensive feature-level analysis under highly non-uniform lighting conditions—e.g., nighttime scenes with localized lighting such as street lamps.***\n\nA3: Thank you for the helpful suggestion. We will add the feature-level analysis under highly non-uniform lighting conditions in Appendix A.3. Our method is motivated by the need to handle highly non-uniform lighting conditions. To this end, we propose the corresponding modeling and an effective method to enhance illumination invariant features for the non-uniform lighting conditions in low-light vision. To validate the effectiveness of the proposed method, we conduct experiments on challenging datasets, including the ACDC-night dataset.\n**The ACDC-night dataset contains diverse real-world scenarios with localized lighting sources like vehicle headlights and street lamps**. \n\nThe visualized segmentation results of the ACDC-night dataset have been provided in Appendix A.3 (Fig.7). In nighttime scenes with localized illumination, our proposed method can extract more robust illumination invariant features without being affected by localized lighting interference for better category recognition and edge preservation thanks to the extended modeling and the effective learnable frequency-domain filter. Furthermore, we will include feature map comparisons under such localized lighting conditions to better illustrate how the model responds in these cases.\n\n\n---\n> ***Q4: On the downstream task side, the work only considers detection and segmentation. It would be valuable to extend the range of downstream tasks to better validate the flexibility and generalizability of the proposed method. Additionally, as the method essentially functions as a feature enhancer trained solely under task-specific losses, further analysis is needed to assess its interpretability and whether the claimed benefits are indeed justified.***\n\nA4: Thank you for your concern. We would like to clarify that our method has already been evaluated across four downstream tasks using four distinct frameworks and four different datasets:  \n&emsp; a. Object Detection (YOLOv3 on ExDark)  \n&emsp; b. Face Detection (TOOD on DarkFace)  \n&emsp; c. Semantic Segmentation (DeepLabV3+ on ACDC-Night)  \n&emsp; d. Instance Segmentation (Mask R-CNN on LIS)\n\nTo further demonstrate the flexibility and task-level generalization of our frequency-domain feature enhancer, we additionally conducted experiments on two more tasks:\n- **Low-light Image Classification** on the CODaN[1] dataset using ResNet-101 as backbone. We compare our method against baseline, FeatEnhancer, and YOLA. As shown in Table 1, our method achieves the best accuracy among all methods(Tab.1).\n- **Low-light Video Action Recognition** on the ARID[2] dataset using the TSN[3] framework (ResNet-50 backbone). Evaluated with Top-1 and Top-5 accuracy, our method again outperforms the baseline significantly(Tab.2).\n\nThese results show that our method can be plugged into diverse vision pipelines.\n\n|Method| Acc | \n| - | - | \n| Baseline | 86.8 | \n| FeatEnhancer | 82.0 | \n| YOLA | 86.4 | \n| Our | **88.2** |\n\n**Table 1:**  Quantitative comparisons of low-light image classification.\n\n|Method|Top-1|Top-5|\n|-|-|-|\n|Baseline|42.65|96.27|\n|Our| **44.84**|**96.53**|\n\n**Table 2:**  Quantitative comparisons of low-light video action recognition.\n\nRegarding **interpretability**, while our method is trained with task-specific losses, it is guided by a physically motivated formulation (Sec.2) that separates illumination and reflectance components in the frequency domain. This formulation leads to two specific design choices: a zero-DC frequency window to suppress global illumination shifts, and a radial basis filter with directional modulation to preserve meaningful structural cues. We will include the above analysis and additional experimental results in the Appendix.\n\n---\n> ***Q5: The performance evaluation lacks comparison with representative joint learning methods, such as MAET, IAT, DANNet, and GLASS-GPS. Including such comparisons would provide a more complete assessment of the method’s effectiveness.***\n\nA5: We appreciate your emphasis on a comprehensive assessment. As shown in Tab.1, the representative joint learning methods **including MAET and IAT**, are categorized under the \"Multi-task\" learning paradigm, and **their results are presented**. \n\nRegarding DANNet[4] and GLASS-GPS[5] (perhaps referring to \"GPS-GLASS\"?), they are both **unsupervised domain adaptation(UDA)** methods, which is different from our supervised learning research. More specifically, DANNet[4] leverages adversarial learning using paired day-night images and pseudo labels from daytime predictions, while GPS-GLASS[5] depends on GPS-aligned image pairs and optical flow from daytime videos to provide pseudo-supervision. Both frameworks are highly specialized for semantic segmentation and rely on specific data, such as scene alignment paired data and optical flow matching, so they are not available in our work. \n\nFor the sake of representativeness and fairness in comparison, we provide the results of methods in four paradigms in low-light object detection in Tab.1. Furthermore, the results of semantic segmentation, face detection, and instance segmentation in low-light vision are presented in Tab.2, Fig.6, and Fig.8. In comparison with a wide range of SOTA methods, our method consistently achieves the best results.\n\n---\n**Reference**\n> [1] Attila Lengyel et al. Zero-Shot Domain Adaptation with a Physics Prior. ICCV 2021.\n\n> [2] Yuecong Xu et al. ARID: A New Dataset for Recognizing Action in the Dark. DL-HAR 2021.\n\n> [3] Limin Wang et al. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition. ECCV 2016.\n\n> [4] Xinyi Wu et al. DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation. CVPR 2021.\n\n> [5] Hongjae Lee et al. GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data. ICCVW 2023."}, {"review_id": "aoDNLDmd51", "reviewer": "Reviewer_tvFq", "summary": "This paper revisits low-light image formation and extends the classical Lambertian model to better characterize low-light conditions. By shifting our analysis to the frequency domain, the authors theoretically prove that the frequency-domain channel ratio can be leveraged to extract illumination-invariant features via a structured filtering process. A plug-and-play module named FRBNet is proposed to be integrated into existing networks for low-light downstream tasks without modifying loss functions.", "questions": "See \"Weakness\".", "limitations": "See \"Weakness\".", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "* Strengths\n  * The proposed method is the first work that operates the channel ratio for illumination-invariant features in the frequency domain.\n  * The figures are very cool, and the performance is good.\n* Weaknesses\n  * The experimental results provide only the downstream tasks. Since this paper is not specific for \"Object Detection and Beyond Under Low-Light Vision\" as FeatEnHancer, the authors should also show the comparisons of the enhanced images with other methods (both numerical results and visual results, otherwise this paper is not comprehensive enough). Besides, the ablation study also provides only the downstream tasks. \n    * Please provide the comparisons like DarkIR to show the enhanced images.\n    * Please also evaluate the performance of low-light enhancement on  LOLBlur dataset [a],  Real-LOLBlur dataset [b], and  LOLv2-Real dataset [c] instead of only evaluate on the downstream applications.\n      * [a] Lednet: Joint low-light enhancement and deblurring in the dark \n      * [b] Real-world blur dataset for learning and benchmarking deblurring algorithms. \n      * [c] Sparse gradient regularized deep retinex network for robust low-light image enhancement \n  * The related work section should be put right after the introduction. Besides, please also include the discussions about the low-light enhancement methods such as [a,b,c].\n  * It seems that the proposed method does not take the camera response function into consideration. The model requires that the camera have a linear response. I'm not sure whether the proposed method has a good generalization ability or not.\n  * Does the proposed method take the dynamic range and pixel saturation into consideration?", "was_revised": true, "final_justification": "Thanks for the rebuttal. I will keep my positive score.", "rebuttal": "**Thank you very much for your positive feedback on our work!**  We have tried to address all the concerns in the following:\n\n---\n\n> ***W1: The experimental results provide only the downstream tasks…***\n\nWe appreciate your thoughtful comments and would like to clarify a potential misunderstanding. Our method is **not aimed at low-level image enhancement** in the low-light sense. Instead, following the motivation of FeatEnHancer and YOLA (which are both also our main comparison methods under PnP), our focus is on improving performance in low-light vision downstream tasks. Therefore, both our main comparisons and ablation studies are centered around detection and segmentation benchmarks. \n\nOur method is designed as a lightweight plug-and-play module for enhancing illumination-invariant features, and is not intended for perceptual restoration. As such, it is not directly suitable for evaluation on restoration benchmarks like LOL[1] or LOLv2[2], which focus on human-perceived image quality (pixel-level image quality evaluation indicators: PSNR, SSIM, etc.). We include visualizations in Figure 3(b). The outputs reflect machine-oriented representations before the backbone, not visually enhanced images(like DarkIR). Our model is trained end-to-end with downstream task losses, and its objective is to boost the quality of low-light visual understanding, rather than the appearance of the image itself. We thank you again for the constructive suggestions and encouragement.\n\n---\n\n> ***W2: The related work section should be put right after the introduction. Besides, please also include the discussions about the low-light enhancement methods such as [a,b,c].***\n\nA2: Thank you for your concerns. We acknowledge that placing the related work section immediately after the introduction is a common and reasonable choice. However, in our case, this is a stylistic decision. We intended for readers to first follow the theoretical motivation and method design after the introduction. This structure is increasingly adopted in recent works, especially for theory-driven papers, and we believe it supports a more coherent flow for readers familiar with the domain. That said, **we fully respect your suggestion and will revise the Section order** accordingly in the final version to align with standard conventions and enhance clarity for a broader audience.\n\nThank you for pointing out these related low-light enhancement works [a, b, c]. We acknowledge that our current paper may underrepresent this, largely due to page constraints and the different focus of our work. Our method is not designed to enhance image appearance, but instead aims to provide a PnP, task-driven solution for low-light vision downstream tasks. As for the suggested traditional enhancement methods and dataset: Retinex-based approaches [c], deblurring datasets like [b], and joint deblurring-enhancement frameworks such as LEDNet [a] are primarily designed to improve image visibility through pixel-level restoration or reconstruction. In contrast, our method doesn’t perform image enhancement, but focuses on learning illumination-invariant features in the frequency domain to support downstream tasks. \n> [a] Lednet: Joint low-light enhancement and deblurring in the dark.  \n> [b] Real-world blur dataset for learning and benchmarking deblurring algorithms.  \n> [c] Sparse gradient regularized deep retinex network for robust low-light image enhancement.\n\nNevertheless, we agree that a broader context is valuable, and we will include discussion of these works in the *Related Work* Section.\n\n---\n\n> ***W3: It seems that the proposed method does not take the camera response function into consideration. The model requires that the camera have a linear response. I'm not sure whether the proposed method has a good generalization ability or not.***\n\nA3: Thank you for pointing this out. While our method is inspired by the physical imaging model, we would like to clarify that our method doesn't strictly require a linear CRF. Instead of explicitly modeling the CRF, our method focuses on extracting discriminative, illumination-invariant features via end-to-end learning with downstream task-specific supervision. The network is trained directly on downstream objectives, which allows it to implicitly adapt to the statistical variations introduced by different CRFs in real-world data. Our experiments span multiple datasets(ExDark, DarkFace, ACDC, LIS) captured by diverse cameras, and our method demonstrates strong generalization performance across all of them. We agree that this is a meaningful aspect. We plan to explore integration with CRF techniques to improve performance in future work.\n\n---\n\n>***W4: Does the proposed method take the dynamic range and pixel saturation into consideration?***\n\nA4: Thank you for this important concern. Similar to the concern of the Reviewer s1m7, dynamic range limitations and pixel saturation are indeed important challenges in low-light scenes. \nWhile our method does not explicitly model saturation effects, it is designed to improve structural robustness through a learnable frequency-domain filter, which helps preserve consistent patterns and suppress the influence of locally overexposed regions.\nTo further evaluate the robustness of our method under pixel saturation, we conducted an additional experiment using the ExDark dataset. We applied a nonlinear gamma transformation to simulate partial overexposure under the Single, Weak, and Screen lighting conditions in ExDark. We then test our method, the baseline, and YOLA(*NeurIPS-2024*) on these transformed images. As shown in Table 1, our method maintains superior performance, confirming that it is more resilient to pixel saturation. We agree that incorporating explicit modeling of dynamic range and saturation effects is a valuable direction, and we consider this a promising avenue for future work.\n\n| Dataset | Method | Recall | mAP |\n| - | --- | --- | --- |\n| |Baseline | 88.7 | 78.7|\n| Exdark_var.   | YOLA (*NeurIPS2024*)| 89.3 | 79.9 |\n| | Our Method| **91.2** | **80.8** |\n\n**Table 1:** Quantitative comparisons based on YOLOv3 detector using ExDark_var.(Overexposure)\n\n---\n***Reference***\n> [1] Chen Wei et al. Deep Retinex Decomposition for Low-Light Enhancement. BMVC 2018.\n\n> [2] Wenhan Yang et al. Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement. TIP 2021."}]

Author rebuttal:

Meta-review: This paper received four borderline, but positive leaning reviews, with all four reviewers converging to borderline accept recommendations (4 BA). 

There was general appreciation for the novelty, motivation and physical grounding of the solution. 

While there were some concerns raised in the original reviews about the quality of the results and the overall presentation, they were addressed to a large extent during the author-reviewer discussion phase. As a result, an accept decision was reached.

The authors are encouraged to account for the reviews while preparing the final camera-ready version.
Decision: Accept (poster)