End-to-End Low-Light Enhancement for Object Detection with Learned Metadata from RAWs
Authors: Xuelin Shen
Haifeng Jiao
Yitong Wang
Yulin HE
Wenhan Yang
Venue: NeurIPS 2025 poster ()
Keywords: Low-light Object Detection
RAW Metadata
Learned Image Compression
Cross-Modal Context
Image Refinement for Machine
Primary area: applications

Abstract: Although RAW images offer advantages over sRGB by avoiding ISP-induced distortion and preserving more information in low-light conditions, their widespread use is limited due to high storage costs, transmission burdens, and the need for significant architectural changes for downstream tasks. To address the issues, this paper explores a new raw-based machine vision paradigm, termed Compact RAW Metadata-guided Image Refinement (CRM-IR). In particular, we propose a Machine Vision-oriented Image Refinement (MV-IR) module that refines sRGB images to better suit machine vision preferences, guided by learned raw metadata. Such a design allows the CRM-IR to focus on extracting the most essential metadata from raw images to support downstream machine vision tasks, while remaining plug-and-play and fully compatible with existing imaging pipelines, without any changes to model architectures or ISP modules. We implement our CRM-IR scheme on various object detection networks, and extensive experiments under low-light conditions demonstrate that it can significantly improve performance with an additional bitrate cost of less than $10^{-3}$ bits per pixel.

End-to-End Low-Light Enhancement for Object Detection with Learned Metadata from RAWs
Xuelin Shen!* Haifeng Jiao!
Yitong Wang*
Yulin He Wenhan Yang*
'Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)
Peng Cheng Laboratory
*College of Computer Science and Software Engineering, Shenzhen University @eenxuelin@gml ac cn, jiaohaifengogml ac cn, wangyitongogml ac cn
heyulin@gml ac. cn, yangwh@pcl.acc ac.
Abstract
Although RAW images offer advantages sRGB by avoiding ISP-induced dis- tortion and preserving more information in low-light conditions, their widespread limited due high storage costs, transmission burdens, and the need for significant architectural changes for downstream tasks. To address the issues, this paper explores
raw-based machine vision paradigm, termed Compact RAW Mctadata-guidee guided Image Refinement (CRM-IR). In particular, we propose Ma- chine iisinn-oriented Image Refinement (MV-IR) module that refines sRGB images to better suit machine vision preferences, guided by learned raw metadata. In detail, we propose a Cross- Modal Contextual Entropy (CMCE) network for raw metadata extraction and compression. It builds upon the latent representation and entropy modeling framework learned image compression methods, and uniquely exploits the contextual correspondence between raw images and their sRGB counterparts to achieve more efficient and compact metadata representation. Additionally, we integrate priors derived from the ISP pipeline to simplify the refinement process, enabling a more efficient design. Such design allows the CRM-IR to focus on extracting the most essential metadata from raw images to support downstream machine vision tasks, while remaining plug-and-pe -nd--ay and fully compatible with existing imaging pipelines, without any changes model architectures ISP mod- ules. We implement our CRM-IR scheme on various object detection networks, and extensive experiments under low-light conditions demonstrate that it can signifi- cantly improve performance with an additional bitrate cost of less than 10-3 bits per pixel. Code is available at httph: thub. www.eaaieeggiaaaaeeeeeeeoeeeeeee wtw.////nnnaa00///MMM
Introduction
Raw images refer to unprocessed and uncompressed data captured directly from camera's image sensor. Their retained sensor readings preserve linear radiance and full bit-depth precision. Raw images typically undergo in-camera Image Signal Processing (ISP) steps, including demosaicing, white balancing, gamma correction and compression, remove perceptual redundancy and enhance visual appeal, ultimately producing the commonly seen sRGB images. However, as these ISP pipelines are primarily designed satisfy human perceptual preferences, they often perform suboptimally in machine vision practice. Especially in low-light conditions, ISP pipelines apply nonlinear radiance amplification to enhance details and textures, inevitably amplifying the inherent noise introduced
"These authors contributed equally. 'Corespondence to: Wenhan Yang.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Compactess:
Flexibiliy:
Efficiincy:
sRGB
sRGB-trsed Method Rewal
bag- IL13
Compactneve:
Flexibility:
Object Delecion
Trainabi ISP
Dexetiin
Object
Effielency:
(b) Raw-busel Method
Ran-bared Mothed Remalts
Laared Campart Mesudass
Metadata Estracior
Compactness:
Flexibiliy:
Effictency:
In-Casera
Metatat-Cautted
Ubject
Ipp
Rar
JRCBB
(c) Method
(d) Comparisons of Different Methods
Figure I: Comparison of existing low-light object detection methodologies: (a) sRGB- based methods, (b) raw-based methods and (c) the proposed CRM-IR. (d) visual results of representative approaches with respect to detection accuracy and transmission overhead, with the first and second rows repro- duced from [16] and respectively.
by physical factors during shooting, such high ISO values, the use of flash and long exposure times [1 2 3
4 [3] Some studies apply low-light enhancement techniques to improve image quality under laser illumination, but these methods are constrained by the information loss during ISP processing 6 77. As result, they can only adjust distributions and fail to fully recover many critical details. Moreover, these methods typically address human perceptual needs but do not sufficiently cater to the preferences or feature spaces required for machine vision, leaving this area of research still open.
Therefore, recent studies have revisited the full utilization of raw images, aiming to exploit their auxiliary and relatively clean visual information to improve machine vision applications 8 9 10). Although the observed performance gains highlight the ad advantages of raw images over processed sRGB images in machine vision tasks, raw-based methods have shown progress but still face signifi- cant challenges that need to be addressed before they can be effectively applied in real applications. In particular, current approaches tend take raw images as direct input [I] [2] and make substantial architectural changes to accommodate the different image format, whereas other studies [ 14 [5] modify the in-camera ISP modules instead. These strategies pose flexibility issues as sRGB-based pipelines remain mainstream and perform well under normal lighting conditions. Moreover, the substantial storage and transmission resources requirements of raw images further hinder the practical adoption of raw-based solutions.
This paper introduces the Compact RAW Metadata-guided Image Refinement (CRM-IR) paradigm to tackle challenges in low-light machine vision, characterized by strong flexibilit and compactesss The approach extracts only essential raw image information, boosting downstream models, and integrates seamlessly into existing vision pipelines without altering current architectures or ISP procedures, shown in Fig. [ IR) module, which uses raw metadata as external conditions for pixel-level modifications to processed sRGB images flexibly. Through joint end-to-end training with metadata extraction and downstream networks, the MV-IR module flexibly adapts to machine vision needs. To ensure compaciness, raw-metadata encoder, based on Learned Image Compression (LIC), is introduced. This encoder extracts and compresses raw metadata efficiently, utilizing cross-modal contextual entropy coding strategy that leverages semantic correspondence between the sRGB image and raw data for effective compression. Additionally, we present the Raw in Dark (RID) dataset, containing 500 annotated RAW sensor pairs from low-light daily scenes, further advancing RAW-based object detection. Extensive experiments demonstrate that CRM-IR achieves superior performance compared existing methods, with minimal metadata transmission of less than 0.001 bits per pixel.
At its core, CRM-IR features lightweight Image Refinement (MV.
Our contributions are summarized as follows:
We propose novel raw-based machine vision paradigm that extracts only the most essential information from raw images to guide machine-oriented sRGB image refinement. This

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design enables seamless integration as plug in within existing BRGB-basee vision pipelines while maintaining minimal storage and transmission overhead.
We introduce a novel raw-metadata encoder that effectively leverages the cross-modal contextual information between processed sRGB and raw images. This enables the proposed scheme to transmit only a minimal amount of raw metadata while significantly enhancing downstream performance.
To advance RAW-based machine vision, we construct Raw in Dark (RID)-a diverse, large- scale dataset of 500 annotated RAW-sRGB pairs captured in real-world low-light scenarios across 8 object categories. RID fills key gaps in existing open-source datasets and serves as a strong benchmark for evaluating generalization in RAW -uidddd object detection through cross-dataset validation.
2
Related Work
2.1 Raw-based Machine Vision
The last decade has witnessed substantial progress in machine vision techniques, markedly improving
ceene-understanding accuracy [18 19 20 21 22 and inference speed [23 24 25 26 and enabling
widespread adoption in real-world applications. Nevertheless, robust performance under low-light conditions remains elusive. Dim illumination limits photon counts and produces a low signal-to-noise ratio 3 4 while compensatory measures such as high-ISO amplification or extended exposure add sensor noise and motion blur [5]: together these factors shift the input distribution away from the priors learned during training and sharply reduce model accuracy. Early studies inserted
preprocessing stage to enhance semantic information before inference [27 28), yet this approach did not achieve satisfactory performance and constrained the models' generalizability and scalability across real-world benchmarks and diverse tasks. Consequently, recent research has turned to raw images, seeking to harness their abundant unprocessed visual information to improve both image enhancement and downstream machine-vision performance. For instance, some studies [TT [2] adopt straightforward strategy by training or fine-tuning downstream models directly raw images, whereas others 13
focus on the ISP pipeline and employ differentiable image signal processors generate sRGB images tailored for machine- viiion tasks. Although these raw-based methods deliver substantial gains, they require full access to raw data, which hampers deployment in edge-to-cloud scenarios where transmitting full-resolution raw images is impractical.
14 15]
2.2 Learned Image Compression
In recent years, Learned Image Compression (LIC) has progressed rapidly in step with deep-learning breakthroughs. In particular, Ballé et al. (29] spearheaded this shift by replacing handcrafted transforms, quantizers and entropy coders with single, fully trainable pipeline. They later augmented their framework with a hyperprior that conditions each latent on auxiliary hyper-latents, markedly improving the rate-distortion trade-off over factorized prior baselines [30]. Follow-up studies refined
the entropy model by exploiting contextual cues: local part-of-image context BI, context spanning
the entire image (32), checkerboard inference patterns 333 and channel-wise context 34 35) have
all been employed to boost accuracy or reduce computation. A recent exemplar, MLIC++ by Jiang et al. [3). fuses local, global and channel contexts in multi-reference entropy model and already surpasses the latest coding standard, Versatile Video Coding (VVC) [37]. Meanwhile, other LIC works tend enhance the analysis-synthesis backbone itself by adopting residual network (38). invertible network [39] and Swin-Transforne 40 411 yielding richer latent representations and
further gains in compression.
Method
3.1 Motivation and Overview
To address the limitations of existing raw-based methods in terms of flexibility and compactness, we propose novel framework called Compact RAW Metadata- Image Refinement (CRM-IR). CRM-IR aims to fully leverage raw images to enhance downstream machine vision tasks while
3

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hv(,), which captures the spatial maintaining a lightweight and pluggable structure compatible with existing vision pipelines. The overall framework is illustrated in Fig. consisting of three key components.
1) Raw Metadata Extraction. Let 2r denote the input raw image and Is represent its sRGB counterpart. At the imaging stage, employ metadata encoder ((;w)) parameterized by w that takes as input 2, while being conditioned on 2s. to identify and extract the raw metadata
[T; Ty:
2) Metadata Coding. Subsequently, hyperprior-based entropy encoder E(., O) parameterized by O adopted to capture the statistical property of ý under multivariate Gaussian distribution, while simultaneously estimating and constraining its entropy, denoted as Ey:; 0) for simplicity, which will be elaborated later.
3) Image Refinement for Vision Tasks. At the application end, an image refinement model (:s Φ) incorporated, being responsible for adapting xi to align with the requirements of downstream machine-vision tasks guided by y.
f. (I.. y: Ø)
where fg is the refined sRGB image, Φ denotes the model parameter.
By feeding the ff the downstream machine vision models A(; the entire CRM-IR scheme is capable end-to-end training under the following constraint,
0, Φ. bb ت arg min
X La(2, (0.v...)) (I)D
Σ
A(M(x,, A(M
bb) + Ely; 0),
(3)
where denotes the training set, consisting of the raw image z,, sRGB image 2, and annotation for machine vision task. LA measures the task performance, while X is the Lagrange parameter to balance the bits cost and downstream task performance.
To meet the dual goals of compactness and flexibility, we incorporate the following designs:
Compactness: We introduce a Cross-Modal Contextual Entropy Encoder (CMCE). The raw metadata must comprise only essential information from ø, while remaining independent of vs with respect to machine vision requirements. CMCE employs the sRGB image &s as a contextual prior during raw metadata compression. This approach effectively captures the inter-redundancy between the raw and the sRGB images.
Flexibility: We design a lightweight Machine Vision-oriented Image Refinement (MV-IR) module, which modifies x, the pixel level under the guidance of metadata y. MV-IR introduces no changes to the image format, allowing seamless integration into existing pipelines without altering downstream architectures.
Detailed descriptions of the proposed CMCE and MV-IR provided in the following subsections.
3.2 Cross-modal Contextual Entropy Encoder
In the field of LIC, context-based entropy modeling involves utilizing surrounding contexts, i.e., information from already encoded latent elements, dynamically estimate the probability distribution for encoding subsequent elements. This approach leverages complex correlations in the input images to improve compression efficiency. Motivated by this, we utilize the cross-modal dependency between the sRGB image x, and raw image zr, both available at the imaging end, to further enhance the compression process. particular, the H, would first concatenated with the Ir, while ensure that its latent elements are leveraged as contextual priors for estimating the probability distribution Zy. After obtaining their joint latent representation y via Equ. (T).
of the latent representation hyperencoder is introduced to extract side information dependencies among the elements of y.
In coding practice, and are typically passed through uniform quantization process to obtain their integer forms ý and for compression purposes. During training, this quantization is approximated using uniform noise 21 (- 2 4). As such, the actual rate estimation can be formulated as,
R R(2) El-logg(Pp (pa [()) + - (pggspss (2)))
(4)

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Cross-Modal Contextual Entropy Encoder
Encoding Process
Ra
hg
Metadata
hg
Main Data Flom
Machine Vision-oriented
N(
Image Refinement
Metndata
Remapping Process
Enhanced sRGil
Demovaicing
Whiie
Comestion
Compession
Inverse Mapping Process
In In-Camera ISP
Overall Framework
Auglooling Dence Lealy BeLU
Conv
ReLU
ReLU
Cone
Dense
Mictadata
Metadatn
X1
(x₂)
Luminance Intensity-
Cosy
Haposs Filter
(b) Inverse Mapping Process
(c) Remapping Process
Figure 2: (a) Overall framework of the proposed CRM-IR framework. (b) and (c) details the inverse mapping and the remapping process of the proposed MV-IR module, respectively.
To obtain (ū))( multivariate Gaussian prior is introduced, parameterized by hyperdecoder h.).),
Pg a (012) (u, o), [M, o] + h.[2;
(5)
where the 0g denotes the parameters of (-). Within this process, an autoregressive context model he is employed to capture the contextual information within ÿ. It utilizes both the side information å and already processed and the already processed portion of ý to predict the distribution of the remaining elements. Accordingly, the causal context for a given latent element is obtained via c he < (e). where Yzs denotes the causal context of y4. The prediction of the Gaussian parameters can be roughly formulated as function involving the hyper-decoder, context model and entropy parameter network,
Hi, he, (2; 0,), Gz 0c).
where (-(:;0)) denotes the entropy parameter network with parameter 0.. Thus, we obtained
1 ( II (N( + u((- 2' (i%).
As for the entropy coding of the side information غ, i.e., the estimation of E[-log2 (p=()) in Eqn. 4 since no hyperprior is available for this process, a simple factorized density model employed. This model is directly adopted from existing LIC works and is not elaborated upon here.
3.3 Machine Vision-oriented Image Refinement based on Raw Metadata
As revealed by existing raw-hased works I within the ISP process, white balancing and gamma correction are the main modules that negatively affect downstream machine vision tasks, as both involve nonlinear mapping of pixel luminance preferences. Therefore, the main idea of the proposed MV-IR is to first convert the sRGB image back to linear space, guided by the raw metadata. Subsequently, an additional remapping process is introduced to enhance details and structures in low-light regions, specifically tailored to machine vision requirements.
discrete domain aligned with human perceptual
In particular, during the inverse remapping process, to simplify the task, we introduce an additional prior based on gamma correction, i.e. we to predict an image-wise gamma correction parameter that is also aligned with the ISP process. Thus, the inverse mapping process can be formulated by,
(I Fam (i),
(8)
5

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Figure 3: Samples from the RID dataset: The first row shows RAW images and the second row shows sRGB images processed by the camera's 's internal ISP.
where 2; denotes the linear representation of Ess and (-) is responsible for predicting the gamma parameter. This prediction is straightforward with the assistance of the raw metadata and can be efficiently implemented using only a few convolutional layers, shown in Fig. (b).
As for the remapping process illustrated in Fig. (c). Frm(.) employed with the aim of learning pixel-wise modulation map conditional on and ý. Considering that our method is specifi- cally designed for low-light scenarios, we introduce a luminance region-wise learning strategy, ie., explicitly learning distinct modulation maps [m,, m2] corresponding to low-light and normal-light regions.
m, Frmm (I., f), E (1,2),
where Fym()) (-) is for the modulation map prediction. They would be subsequently merged m via through luminance intensity-bypass filtering operations.
m, 6x(x)).
(10)
where (-) and 62(-) denote pixel-wise bypass filters that selectively pass the corresponding elements of the learned maps when the pixel intensities fall within the low-light and normal light regions, re- spectively. Afterward, the modulation map m is applied to the linear representation 2; via summation and subsequently fed into lightweight fusion layers (,(.) for further adjustment, producing the final output.
f, Fy(m +mn+20 a.).
(11)
3.4 Raw in Dark(RID) Dataset
This subsection introduces the RID dataset to enable cross-dataset generalization validation, given the scarcity of open-source real-world benchmarks. Compared with the widely employed raw-based object detection dataset LOD 42]. RID broadens the range of scenes and object categories and places particular emphasis on images captured under extremely low-light conditions. As for the source image collection, a Canon EOS8OD was used to capture 500 paired samples, each consisting of RAW image and its sRGB-JPEG counterpart. We selected scenes that span a range of everyday environments, including dimly lit underground parking garages, nighttime roadsides and poorly illuminated indoor rooms, all characterized by deep shadows and severely limited visibility. We selected ISO settings of 50 and 100 and exposure times of 1/125s and 1/250s to replicate the photon-starved conditions typical of extremely dark surveillance scenarios.
Regarding the annotations, professional annotators are employed to create accurate instance-level labels for widely studied classes, i.e., car, bicycle, chair, to support cross-task generalization validation, and further categories including person, zebra crossing, emergency exit sign, fire extinguisher and dustbin are included to extend the dataset's 's coverage. Representative examples are exhibited in Fig. B It is worth noting that RID is an ongoing project. We plan to further expand the dataset by employing various shooting devices, including different cameras and smartphones, with the goal of investigating the influence of diverse ISP procedures. Additionally, we will include more annotations for other critical machine vision tasks, such as semantic segmentation and instance segmentation, aiming to support the broader machine vision research community.

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Table 1: Quantitative comparison results on the YOLOv3 backbone. The bold and underlined font indicate the best and second-best results, respectively. For reference, JPEG-format sRGB images have an average bpp of 0.15, whereas raw images require 48 bpp.
In-Dataset Val. (LOD)
Dots-eDttset Val. (RID)
Method
mAP
mAP50
mAP75
bpp
mAP
mAP50
mAP75
hpp
YOLOv3
40.00
67.67
43.44
45.67
65.80
63.75
Zero-DCE
41.19
67.79
45.79
45.26
64.38
62.76
KinD
41.22
67.03
44.60
44.37
63.76
58.55
YOLA
41.00
68.49
45.87
38.29
60.22
46.29
RAOD
41.60
70.49
42.29
39.74
68.49
41.36
Ours
42.14
69.11
46.02
4.88e-4
51.72
65.81
63.85
4.86e-4
Experiments
At the experimental stage, implemented our CRM-IR scheme set of object detection models and conducted extensive comparisons with other low-light object detection paradigms, including both sRGBB-based and raw-based methods, demonstrate the superiority of our approach. Moreover, comprehensive ablation studies were performed to validate the effectiveness of the proposed CMCE and MV-IR modules.
4.1 Experimental Settings
Datasets. Low-light Object-Detection (LOD) dataset is employed as our benchmark, which contains 2230 paired RAW and sRGB-JPEG format image pairs collected by Canon EOS 5D Mark IV camera, covering both low-light and normal daylight scenes, where only the low-light parts are selected in experiments. In particular, 1,784 training pairs and 446 test pairs are selected for model training and evaluation, respectively. These images contain a total of 9,726 labeled instances spanning 8 common object classes: car, motorbike, bicycle, chair, dining table, bottle, TV monitor and bus. In addition, our proposed RID dataset is employed to perform cross-dataset testing, aiming to evaluate the generalization capability of the employed methods.
Baselines. Three milestone object detection models are employed as baseline models, including YOLOv3 43 Faster R-CNN [44] and CenterNet [21]. Moreover, four state-of-the-art low-light object detection schemes are evaluated, including three sRGB-based methods: Zero-DCE [6]. KinD [4 and YOLA [46). all of which follow similar pipeline that first enhances low-light images before feeding them into downstream object detection models. Moreover, one raw-based method RAOD 17 is also adopted. It follows similar enhancement-basee pipeline by leveraging raw images to guide the enhancement process, but it overlooks the transmission cost of raw data. For a fair comparison, all competing schemes are implemented on the same baseline models our method and trained from scratch using the same dataset.
Evaluation Criterion. We adopt the commonly used mean average precision regarding mAP, mAP50 and mAP75 to measure downstream task performance. Moreover, to demonstrate the compactness of our method, we report the hpp as an indicator of storage or transmission resource requirements across different methods. For the employed methods, we report the bpp of sRGB images and raw images for sRGB-bass and raw-based approaches, respectively. In contrast, for our method, additionally report the bpp of the raw metadata alongside the sRGB image.
Implementation Details. During training, data augmentation strategies are employed, including random horizontal flips and random scale jitter during resizing. All models were trained for 300 epochs using the Adam optimizer [47]. A linear scaling learning rate with a cosine decay schedule was employed, starting from an initial learning rate of 5e-4. The weight decay was set to O, momentum was 0.9 and the training batch size was set to 8. During both training and testing, all input images were resized to 512 x 512. All experiments were conducted using PyTorch on an NVIDIA RTX 6000 Ada Generation GPU with 48 GB of memory.
7

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Table 2: Quantitative comparison results on the Faster R-CNN backbone. The bold and underlined font indicate the best and second-best results, respectively.
In-Dataset Val. (LOD)
Val. (RID)
Method
mAP
mAP50
mAP75
hpp
mAP
mAP50
mAP75
bpp
Faster R-CNN
39.96
67.12
41.74
37.23
63.79
39.30
Zero-DCE
40.50
67.94
42.56
37.73
64.57
40.07
KinD
39.83
66.86
42.23
37.11
63.54
39.76
YOLA
40.31
68.22
42.31
37.55
64.83
39.84
RAOD
41.32
69.79
43.14
38.50
66.33
40.62
Ours
41.75
68.49
43.94
3.26e-4
38.89
65.09
41.37
3.23e-4
Table Quantitative comparison results on the CenterNet backbone. The bold and underlined font indicate the best and second-best results, respectively.
In-Dataset Val. (LOD)
Cross-Datasel Val. (RID)
Method
mAP
mAP50
mAP75
bpp
mAP
mAP50
mAP75
bpp
CenterNet
40.41
65.36
42.60
40.33
62.63
44.67
Zero-DCE
41.44
65.85
44.90
40.30
62.64
44.53
KinD
40.10
62.49
44.23
40.28
62.48
44.52
YOLA
41.74
66.18
44.55
42.04
66.48
44.85
RAOD
42.89
69.25
43.94
43.45
70.28
43.22
Ours
42.05
68.09
44.52
1.38e-3
42.31
68.28
44.91
1.39e-3
4.2 Experimental Results
The quantitative evaluation results regarding the baseline models of YOLOv3, Faster R-CNN and CenterNet are established in Table Table Table respectively. First, the effectiveness of the proposed method can be easily observed comparing with the baseline models, as leading to an average mAP improvement of 2. 2.14%, 1.79% and 1. 1.64% regarding YOLOv3, Faster R-CNN and Centernet, respectively. Considering bitrate performance, our method introduces only 4.88e-4, 3.26e-4 and 1.38e-3 bpp of raw-metadata overhead for the three baseline models, respectively. Relative to the original sRGB bitrate of 0.15 hpp. this increase under 0.5 percent and therefore negligible in practice, thanks the CMCE module's ability to capture and exploit the contextual correspondenec between each raw image and its sRGB counterpart.
Compared with state-of-the the-art sRGB-based methods Zero-DCE, KinD and YOLA, our scheme achieves an average mAP improvement of about 0.94%, 1.60% and 0.96%, confirming that lever- aging raw-image information effectively overcomes the limitations of conventional ISP processing. Compared with the raw-based method RAOD, our approach achieves comparable performance while transmitting only compact metadata stream instead of the entire raw image. RAOD benefits from full access to the 48 bpp raw data, yet this requirement imposes prohibitive storage and transmission load, making the scheme impractical for many real-world deployments. Cross-dataser evaluations confirm the strong generalization capability of our method, as maintains nearly the same level of object-detection accuracy in unseen scenarios.
Fig. provides set of intuitive comparisons. Things have to be mentioned that, distinct visualization strategies were adopted for a clear representation. For the Ground Truth, detection bounding boxes are visualized directly on the original low-light input. For the other approaches that follow the "enhanceeenttthee- diiieendttettio"" pipeline, detection results are shown on their corresponding enhanced images. Fig. 4 demonsttatess that low light sRGB images contain considerable environmental noise that the enhancement stage cannot fully remove, causing frequent mis-detections. By contrast, incorporating raw information reduces noise the enhance version and substantially improves downstream detection accuracy.
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Ground Truth
Zero-DCE
KinD
YOLA
RAOD
Ours
Figure 4: Visualization of object-detection results: the first, second and third rows correspond to methods based on YOLOv3, Faster R-CNN and CenterNet, respectively.
4.3
Ablation Studies
To evaluate the distinct contributions of our proposed modules and the utility of leveraging image data, we performed a detailed ablation study on the LOD datasets. We compare our full model against several ablated variants:
w/o CMCE: In this configuration, the metadata extraction and compression stages are disabled, so no raw metadata are passed to the MV-IR module. The MV-IR remains active but is driven by non-informative placeholder implemented as zero tensor.
w/o MV-IR: We retain the CMCE module but employ the enhancement module with simple concate- nation, where the extracted metadata and the sRGB image features are combined via concatenation.
The ablation results are presented Table 4 where the effectiveness of each specially designed module is evident. As shown, excluding the metadata yields average mAP50 decrease of about 0.58%, 2.99% and 2.24% regarding YOLOv3, Faster R-CNN and CenterNet, respectively. Meanwhile, omitting the MV-IR module results in an overall 1.91%, 3.26% and 1.77% decrease in YOLOv3, Faster R-CNN and CenterNet, respectively.
Table Ablation Study Results of CMCE and MV-IR modules
Detector
CMCE
MV-IR
mAP
mAP50
mAP75
X
X
40.00
67.67
43.44
X
V
41.06
68.53
44.78
YOLOV3
X
41.19
67.20
45.21
V
42.14
69.11
46.02
X
x
39.96
67.12
41.74
X
39.65
65.85
42,40
Faster R-CNN
X
38.84
65.23
41.13
41.75
68.49
43.94
X
X
40.41
65.36
42.60
X
41.16
65.85
43.67
CenterNet
X
41.64
66.32
44.25
42.05
68.09
44.52
5 Conclusion
To conclude, this paper explores a new raw-based object-detection paradigm called Raw Metadata-guidee guided Image Refinement (CRM-IR). Compared with existing raw-based approaches,

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CRM-IR is characterized by its flexibility and compactness: it integrates seamlessly into current machine vision pipelines while explicitly accounting for the transmission and storage overhead of raw information. For flexibility, we introduce Machine Vision-oriented Image Refinement (MV-IR) module that adjusts sRGB images to better match machine vision preferences, functioning as a stan- dalone preprocessing step without altering network architectures or in-camera ISP modules. CRM-IR further leverages raw metadata collected at the imaging stage; by extracting only the essential raw information and using the paired sRGB image contextual prior, it delivers significant gains in downstream object-detection accuracy with negligible additional bitrate.
Acknowledgments
This work was in part by the Interdisciplinary Frontier Research Project of PCL under Grant 2025QYB013, in part by the Major Key Project of PCL (PCL2025A03), in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A15150 Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) under Grant No. GML-KKF24-27" in part by the Natural Science Foundation of Guangdong Province under Grant 202341515011667.
in part by the Open
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Reviews:
[{"review_id": "U6ApDjPiZb", "reviewer": "Reviewer_4Xo9", "summary": "A novel low-light object detection paradigm, Compact RAW Metadata-guided Image Refinement (CRM-IR), is proposed. The core idea of this method is to avoid directly using RAW images for inference, and instead, leverage compressed RAW metadata to guide the enhancement of sRGB images. This enables improved object detection performance under low-light conditions without modifying existing detection models or the camera's ISP pipeline.", "questions": "How does the metadata-guided inverse gamma and remapping in MV-IR fundamentally differ from traditional enhancement methods like Retinex?  Does it truly capture representations more suitable for machine vision?\n\nThe CMCE achieves extremely low bitrate (<0.001 bpp).  What specific cross-modal redundancies does it exploit, and how does it improve upon standard learned image compression techniques?\n\nThe evaluation primarily uses YOLOv3, Faster R-CNN, and CenterNet as baselines, which are relatively outdated compared to recent state-of-the-art detectors such as YOLOv11/v12,. This may limit the assessment of the method’s effectiveness in modern real-world deployment scenarios.\n\nThe paper does not analyze the runtime or computational overhead introduced by the MV-IR and CMCE modules.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Quality: The authors are based on raw and SRGB pairs of low light, but there seem to be limitations in the experimental results (many indicators are worse than YOLA RAOD) . \n\nClarity: Low-Light Object Detection combined with Metadata from RAWs is natural and well-motivated, and the paper presents a clearly structured framework. \n\nSignificance: The method requires no modifications to the ISP or downstream detection model architectures, making it easy to integrate into existing vision pipelines. It achieves comparable object detection accuracy while significantly reducing RAW data storage and transmission costs by transmitting only compact metadata instead of full-resolution RAW images.\n\nOriginality: The novelty of this approach appears limited, as its performance gains seem inferior to both YOLA (sRGB-based) and RAOD (raw-based) methods. This raises legitimate doubts about whether the cross-modal design actually delivers meaningful improvements.", "was_revised": true, "final_justification": "Thank authors for the rebuttal. The authors have addressed most of my concerns. However, the current method still shows some limitations in enhancing sRGB images. Nevertheless, considering the valuable contribution to RAWs-based low-light enhancement, I am willing to raise my rating to a positive recommendation.", "rebuttal": "First of all, we would like to express our sincere appreciation for your responsible and comprehensive review. Your positive attitude toward our RAW-metadata-guided, plug-and-play design is a great encouragement to us. Regarding your concerns, we hope the following point-by-point responses address them, and we would appreciate any further suggestions.\n\nQ1. Concerns about the fundamental differences from traditional enhancement methods.\n\nAns.: Thanks for your comments. First, we would like to emphasize that, compared with traditional Retinex-based methods whose heuristic illumination estimation is agnostic to camera settings and recovers only approximate reflectance, our approach begins in the RAW domain to reconstruct the linear sensor-radiance domain. The inverse-gamma and remapping operations are derived analytically from the RAW images, making the mapping physically interpretable.\n\nBesides our work, recent RAW-based low-light-enhancement methods have become mainstream and demonstrate overwhelming advantages over traditional approaches. Compared with these RAW-based methods, we focus on practical edge-to-cloud scenarios, where RAW images cannot be directly accessed, and aim to minimize transmission cost by identifying and extracting only the most essential RAW data.\n\nFor intuitive demonstration, we include two Retinex-based methods, one conventional method LIME (*LIME: Low-Light Image Enhancement via Illumination Map Estimation*，TIP 2016) and one learning-based method RetinexFormer (*Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement*, ICCV 2023). We first compare their performance with ours on the LOD dataset, then feed their outputs into our pipeline for additional RAW-metadata-guided enhancement; the corresponding results are shown below.\n\n| | Ours | LIME | RetinexFormer | LIME+OURS | RetinexFormer+OURS |\n|---|---|---|---|---|---|\n| mAP50 | 69.11|67.56 |65.95 | 68.91|67.77 |\n| bpp | 4.88e-4 | - | - | 4.92e-4 | 4.77e-4 |\n\nAs shown, our method outperforms the conventional methods and can further enhance their performance, demonstrating that RAW metadata offers remarkable advantages over these approaches.\n\nQ2. Explain the specifically leveraged specific cross-modal redundancies and improvement upon standard learned image compression. \n\nAns.: Thanks for your comments. Cross-modal redundancy denotes the contextual correspondence between a RAW image and its compressed sRGB counterpart, both of which are available at the imaging end. Because the sRGB image is a degraded yet closely correlated representation of the same scene, the two modalities share substantial mutual information. This allows our method to transmit only a small amount of RAW-specific data, since the decoder already possesses the sRGB frame. In contrast to standard learned image codecs, which handle a single modality and remove only intra-image redundancy, we take as input both RAW and sRGB and improve the entropy model to exploit their correspondence, thereby further reducing the required bitrate.\n\nMoreover, our method does not aim for signal-level fidelity, meaning it does not reconstruct the RAW image on the decoding side. The end-to-end strategy uses compactness and task-specific performance as its constraints, so it extracts only the key information needed by downstream tasks. These task-oriented adaptations, compared with existing learned image codecs, enable an extremely low bitrate.\n\nQ3. Evaluate the effectiveness on recent detectors.\n\nAns.: Thanks a lot for your kind suggestion. Due to time and computational-resource limitations, we were only capable of adopting only YOLOv11 (-x version) as the extra benchmark, we hope your understanding. Comparisons were conducted on the LOD dataset, with all settings kept exactly the same as in our manuscript for fair comparison; the results are shown below.\n\n| | mAP | mAP50 | mAP75 | bpp |\n|---|---|---|---|---|\n| YOLOv11 | 50.59 | 70.12 |54.82  | -|\n| Zero-DCE | 50.28 | 69.58 | 54.82 | -|\n| KinD | 48.70 | 68.47 |52.93  | -|\n| YOLA | 50.99 | 70.84 | 55.95 | -|\n| RAOD | 51.87 | 71.56 | 56.04 | -|\n| Ours | 52.05 | 71.22 | 56.37 | 7.25e-4 |\n\nThe results show that our method still obtains remarkable advantages over these enhancement-then-detection methods on recent detectors. We will include additional results using other SOTA detectors in the supplementary materials of the revised manuscript, as this is essential for a comprehensive demonstration of our method’s effectiveness. Thanks again for your valuable suggestion.\n\nQ4. Analyze the runtime and computational overhead.\n\nAns.: Thanks for your valuable suggestions, it is indeed important and necessary to analyze the running speed and computational overhead for reference. \n\nAs our method is mainly proposed for reducing the transmission cost in edge-to-cloud scenarios, where the source images (sRGB or RAW) also need to be compressed and transmitted, we benchmark both stages separately for a fair assessment of runtime and computational overhead.All GPU experiments run on an NVIDIA RTX 6000. The resulting speed and computational-cost statistics are reported below.\n\n1)Imaging end: We compare the CMCE encoder with extra two image codecs (Encoder part only): TIC (*Transformer-based Image Compression*, DCC 2022), and MLIC++ (*Linear-Complexity Multi-Reference Entropy Modelling*, arXiv 2023).;\n\n| | Para. (M) | Enc. Latency (ms) | GFLOPs |\n|---|---|---|---|\n| TIC | 12.74 | 3009.13 |338.59  |\n| MLIC++ |68.80  | 181.97 | 147.70 |\n| Ours (CMCE) | 1.78 | 40.72 | 38.17 |\n\n2)Application end: we evaluate the MV-IR module against the five enhancement methods already employed in our manuscript. \n\n| | Para. (M) | Inf. Time (ms) | GFLOPs |\n|---|---|---|---|\n| Zero-DCE | 0.08 | 6.50 | 20.76 |\n| KinD | 8.02 | 5.23 | 258.91 |\n| YOLA | 0.01 | 2.00 | 2.32 |\n| RAOD | 0.07| 1.75 | 0.21 |\n| Ours (MV-IR) | 0.08 | 2.52 | 0.27 |\n\nQ5. Concerns about the performance gains to both YOLA (sRGB-based) and RAOD (raw-based) methods. \n\nAns.: Thanks for your comments. 1)As for for the sRGB-based method YOLA, we outperform it in 17 of 18 comparison groups (2 datasets $\\times$ 3 backbones $\\times$ 3 metrics); notably, in cross-dataset testing our method achieves a 4 % mAP50 gain across all three backbone detectors, clearly demonstrating the effectiveness of leveraging RAW information.\n\nQ6. Moreover, compared with RAOD and other RAW-based methods, we are not focused solely on achieving higher performance, which would be unrealistic because those approaches have full access to the entire RAW data. Our primary goal is to minimize the transmission and storage requirements of RAW-based pipelines so they can be deployed widely in edge-to-cloud scenarios. Hence, evaluating only downstream accuracy is an unfair comparison, given that RAW images typically require 48 bpp for storage and transmission."}, {"review_id": "dT8sxLCeoD", "reviewer": "Reviewer_VyZs", "summary": "This work proposed an approach for low-light image enhancement by partially using the raw sensor data. \nIt estimates the metadata from the raw image using a trainable probabilistic encoder, then uses the metadata to remap the sRGB image to a domain optimal for machine vision tasks. Main novelties include:\n\n1. An entropy parameter network for the metadata extraction\n2. A selective remapping technique that treats the low-light part and the high-light part of the image separately.", "questions": "1. The ablation study is done on the latest generations of detection models.  YOLA and RAOD, which are compared in Tables and 3, are not used in the ablation study. Can the authors explain the reason of it?", "limitations": "N/A", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. This work uses metadata for the image enhancement, which is less computationally expensive than those that directly use the raw image.\n2. This work does not require a trainable ISP for the raw data, thus it is expandable to different schemes. \n\nWeakness:\n1. The estimation of bpp is not stated clearly. Is it purely the transmission and storage overhead for the metadata? If so, the computational cost of the encoder is not counted. A proper comparison of the total FLOPs against other models might be useful.", "was_revised": true, "final_justification": "As the authors explained my questions regarding the metric comparison, I am willing to recommend this paper as accept.", "rebuttal": "First of all, we would like to express our deepest gratitude for your positive attitude and valuable suggestions, which are very important and a great encouragement to us. We hope that our following responses adequately address your concerns.\n\nQ1. Explain the bpp calculation. \n\nAns.: Thanks for your thoughtful review, and we apologize for any confusion caused by the missing details. The bpp values in our manuscript are measured at test time and accurately represent the actual transmission cost in practical deployments. During inference, the RAW metadata generated by the CMCE module is coded into a binary bit-stream and transmitted to the application end. Its bpp is calculated as the bit-stream size (in bits) divided by the image resolution. The bpp for both the sRGB and RAW reference images is computed in the same way, using each file’s size (in bits) divided by its resolution.\n\nQ2. Compare the computational cost against other models.\n\nAns.: Thanks for your valuable and constructive suggestions.  It is indeed important and necessary to analyze the running speed and computational overhead, which are critical in real-world applications.\nAs our method is mainly proposed for reducing the transmission cost in edge-to-cloud scenarios, where the source images (sRGB or RAW) also need to be compressed and transmitted, we benchmark both stages separately for a fair assessment of runtime and computational overhead.All GPU experiments run on an NVIDIA RTX 6000. The resulting speed and computational-cost statistics are reported below.\n\n1)Imaging end: We compare the CMCE encoder with extra two image codecs (Encoder part only): TIC (*Transformer-based Image Compression*, DCC 2022), and MLIC++ (*Linear-Complexity Multi-Reference Entropy Modelling*, arXiv 2023).; \n\n| | Para. (M) | Enc. Latency (ms) | GFLOPs |\n|---|---|---|---|\n| TIC | 12.74 | 3009.13 |338.59  |\n| MLIC++ |68.80  | 181.97 | 147.70 |\n| Ours (CMCE) | 1.78 | 40.72 | 38.17 |\n\n2)Application end: we evaluate the MV-IR module against the five enhancement methods already employed in our manuscript. \n\n| | Para. (M) | Inf. Time (ms) | GFLOPs |\n|---|---|---|---|\n| Zero-DCE | 0.08 | 6.50 | 20.76 |\n| KinD | 8.02 | 5.23 | 258.91 |\n| YOLA | 0.01 | 2.00 | 2.32 |\n| RAOD | 0.07| 1.75 | 0.21 |\n| Ours (MV-IR) | 0.08 | 2.52 | 0.27 |\n\nQ3. Concerns of the absence of YOLA and RAOD in ablation studies.\n\nAns.: Thanks for your kind suggestions. We apologize for any misunderstanding caused by our unclear representation. To address your concern, we would like to clarify that in Tables 1–3 we compare the commonly employed **restoration-then-detection** paradigm: that first apply a low-light enhancement model to obtain high-quality images, then feed these images to downstream object-detection models. In fact, both YOLA and RAOD are machine-vision-tailored low-light enhancement methods; thus, we did not employ them in the ablation study. After carefully proofreading the manuscript, we realized that our statement in line 235 may mislead readers, and we will modify the corresponding part in the revised manuscript to avoid this issue."}, {"review_id": "L4eBeWzVst", "reviewer": "Reviewer_rFZh", "summary": "The authors present CRM-IR (Raw Metadata-guided Image Refinement), a novel raw-based object detection framework that achieves superior performance compared to conventional methods while excelling in flexibility and compactness. \nTo realize this, the authors designed two key components: CMCE and MV-IR, and introduced a dedicated evaluation dataset.", "questions": "Please refer to the weaknesses section. My current rating could change significantly depending on the authors’ responses. While I have some knowledge of camera ISP and image restoration, I do not have specialized expertise in the specific field of this study. I am aware of this and will actively refer to other reviewers’ questions and the authors’ responses as well.", "limitations": "The authors do not mention any limitations of this study. I hope they will add this to Section 5. Conclusion, and I would like to hear their thoughts on this point in the rebuttal.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "[Strengths]\n1. The main motivation is simple, and reasonable.\n2. The new evaluation dataset was provided.\n3. The paper is well-organized and easy to read.\n\n[Weaknesses]\n1. It is questionable whether each module was trained as intended by the authors and whether this led to the observed performance improvements. For example, Section 4.3 only shows the performance degradation when CMCE and MV-IR are simply removed. Based on these experimental results, it is difficult to verify whether CMCE effectively captures the inter-redundancy between the raw and the sRGB images as intended by the authors, and whether the inverse mapping from the linear representation in MV-IR is being performed as intended.\n\n2. The baseline models(e.g. YOLOv3(2018), Faster R-CNN(2015), CenterNet(2019)) used are outdated, and the performance trends vary significantly across baselines, raising doubts about whether the authors' proposed method would bring meaningful performance improvements in recent models. For example, in Table 3, when CenterNet(2019) is used as the backbone, the performance differences between YOLA, RAOD, and the proposed method(Ours) are not statistically significant. This trend is likely to become more pronounced when evaluated against newer models. Additionally, I am curious why there was no comparison with models such as AdaptiveISP (NeurIPS 2024).\n\n3. This model is specialized for the ISP of the camera used to capture the training data, and its practical application assumes imaging with the target camera. In this context, measuring generalization ability on datasets captured with \"different cameras\" seems questionable. If the LOD dataset lacks sufficient extremely low-light conditions, collecting additional evaluation datasets should use the same camera to ensure meaningful comparisons.sets should involve the same camera to ensure meaningful comparisons.\n\n4. I'm curious why camera parameters that directly influence the ISP processing, such as exposure time and ISO, were not utilized.\n\n5. I am curious why the mAP75 performance of RAOD is particularly low except when using Faster R-CNN as the backbone.\n\n6. In the table, all the bpp values for the comparison models are marked as \"-\", is this report accurate?\n\n7. The authors do not mention any limitations of this study. If the authors have any limitations in mind, I would appreciate it if they could describe them in detail.\n\n8. Minor error.\n-- L142: y >> y_hat\n-- L164: (redundant phrase)\n-- L172: Shouldn't a reference be provided?\n-- L266: YOLOA >> YOLA\n-- All tables: map >> mAP", "was_revised": true, "final_justification": "The authors have addressed all of my concerns, and they have been fully resolved.", "rebuttal": "First of all, we want to express our sincere appreciation for your responsible and thoughtful review. Your suggestions not only help improve the quality of our current manuscript but also inspire our future work. Below, we answer your questions point-by-point in the hope of addressing your concerns, and we welcome any further suggestions.\n\nQ1: Concerns about the effectiveness of the proposed modules and the ablation study settings.\n\nAns.: Thank you for your valuable and insightful suggestions, which made us realize that our current ablation studies are insufficient to demonstrate the effectiveness of our design.\n\nFor the ablation of CMCE, we conducted an additional study that replaces the referenced sRGB images with plain images (containing no information) at the encoding side, while keeping all other components identical. This eliminates cross-modal correspondence during RAW-metadata compression, and we retrained the entire framework on the YOLO-V3 backbone. The results are listed below:\n\n| | mAP | mAP50 | mAP75 | bpp |\n|---|---|---|---|---|\n| Ours(Full) | 42.14 | 69.11 | 46.02 | 4.88e-4|\n| Ablated | 39.40 | 66.50 | 43.39 |0.25 |\n\n\nAs clearly shown, omitting the correspondence between the sRGB and RAW images greatly increases the transmission-resource requirement and results in a remarkable performance drop, confirming that CMCE successfully leverages cross-modal redundancy.\nAs for MV-IR, we conducted comprehensive ablation studies by alternately disabling the remapping and inverse-mapping steps to demonstrate their effectiveness on the YOLOv3 backbone; the results are shown below.\n\n| | mAP | mAP50 | mAP75 | bpp |\n|---|---|---|---|---|\n| Ours (Full) | 42.14 | 69.11 | 46.02 |4.88e-4 |\n| $w/o$ inv. mapping | 40.59 | 66.17 | 44.22 | 8.57e-4|\n| $w/o$ remapping |40.85  | 67.67 | 43.93 | 6.60e-4|\n\nThank you again for your insightful suggestions. We will incorporate these new ablation studies into the revised manuscript.\n\nQ2. Evaluate the effectiveness on recent detectors.\n\nAns.: Thank you for your suggestions. In response, we have added the cutting-edge object detector YOLOv11 as a new baseline and compared it with other restoration-then-detection schemes. The corresponding results are listed below.\n\n| | mAP | mAP50 | mAP75 | bpp |\n|---|---|---|---|---|\n| YOLOv11 | 50.59 | 70.12 |54.82  | -|\n| Zero-DCE | 50.28 | 69.58 | 54.82 | -|\n| KinD | 48.70 | 68.47 |52.93  | -|\n| YOLA | 50.99 | 70.84 | 55.95 | -|\n| RAOD | 51.87 | 71.56 | 56.04 | -|\n| Ours | 52.05 | 71.22 | 56.37 | 7.25e-4 |\n\nThe results show that our method still obtains remarkable advantages over these enhancement-then-detection methods on recent detectors. We will include additional results using other SOTA detectors in the supplementary materials of the revised manuscript, as this is essential for a comprehensive demonstration of our method’s effectiveness. Thanks again for your valuable suggestion.\n\nQ3. Comparison with recent enhancement models \n\nAns.: Thanks a lot for your suggestions. Following your suggestion, we have added two RAW-based enhancement methods AdaptiveISP (*Learning an Adaptive Image Signal Processor for Object Detection*，NeurIPS 2024) and SID (*Learning to See in the Dark*, CVPR 2018) for a more comprehensive comparison. Owing to time and computational-resource constraints, we are able to report results only on the YOLOv3 backbone; these results are shown below. We appreciate your understanding.\n\nHerein, we’d like to emphasis that compared with exiting RAW-based methods, we are not **focused solely on achieving higher performance**, which would be unrealistic because those approaches have full access to the entire RAW data. Our primary goal is to minimize the transmission and storage requirements of RAW-based pipelines so they can be deployed widely in edge-to-cloud scenarios, given that RAW images typically require 48 bpp for storage and transmission. \n\n| | mAP | mAP50 | mAP75 | \n|---|---|---|---|\n| YOLOv3 | 40.00 | 67.67 |43.44  | \n| SID | 40.01| 68.69 |42.84  | \n| AdaptiveISP | 41.91 | 68.55 | 45.66 | \n| Ours | 42.14 | 69.11 | 46.02 | \n\nHowever, quite surprisingly, our method still outperforms these newly incorporated RAW based methods, which can be mainly attributed to the analytical design of our MV-IR module. It establishes a clear and efficient restoration path built upon prior knowledge of the ISP pipeline. Moreover, we will incorporate more recent RAW-based methods into our related-work section to better contribute to the community.\n\nQ4. Concerns about the generalization evaluation across different cameras and the validity of cross-camera comparisons.\n\nAns.: Thanks a lot for your kind and valuable suggestions. Herein, we would first like to emphasize that both the camera we used and the one used for the LOD dataset are Canon cameras. Although they are different models, the RAW images were captured at the same format. Moreover, our model is designed to extract only the essential metadata from the information that is discarded by the ISP. Since most information loss in the ISP occurs during gamma correction and compression (processes that depend only minimally on the camera model) the MV-IR module applies an inverse-gamma operation followed by a learnable remapping to recover the information lost. From this perspective, we hold a positive attitude toward our model’s cross-camera generalization capability.\n\nQ5. Explanation for not utilizing exposure time and ISO\n\nAns.: Thanks for your comments. First, we acknowledge that exposure time and ISO are important and are widely used in RAW reconstruction and low-light enhancement studies which pursues high reconstruction fidelity or perceptual quality. However, our method focuses on fulfilling machine-vision needs while minimizing transmission cost, as we apply no fidelity or perception-oriented supervision.\n\nMoreover, we would like to claim that with the access to both the sRGB images and the RAW counterpart, these parameters can be easily derived analytically, in other words, the obtained sRGB images have already contained such information.\n\nAccordingly, we do not employ these camera parameters directly; instead, we rely on end-to-end training to automatically extract the most essential metadata.\n\nQ6. Concerns regarding the unexpectedly low mAP75 performance of RAOD\n\nAns.: Thanks a lot for your comments. According to our analysis, the discrepancy stems from the training preferences of different object detection models. Factors such as higher NMS thresholds, the choice of GIoU/DIoU/CIoU losses, and bounding box regression bias can influence mAP50 and mAP75 differently. Since most detectors prioritize the mAP50 metric, their hyperparameters are often tuned to favor it. As a result, performance comparisons between mAP50 and mAP75 may exhibit different trends. Moreover, we strictly adhere to the principle of reproducibility and will open-source all checkpoints and training scripts for the baseline models.\n\nQ7. Concerns regarding the accuracy of the reported bpp values\n\nAns.: Thanks for your kind comments. We apologize for any confusion caused by our unclear representation. In Tables 1–3 of our manuscript, the bpp value accounts only for the additional bits required to transmit the RAW metadata. In fact, as noted in lines 263 and 272 of our manuscript, the bpp values for the sRGB and RAW images are 0.15 and 48, respectively. Therefore, the total cost in our setting is 0.15 (sRGB)+ 4.88e-4(RAW Metadata). We will revise the manuscript to make this point clearer.\n\n Moreover, the bpp values in our manuscript are measured at test time and accurately represent the actual transmission cost in practical deployments. During inference, the RAW metadata generated by the CMCE module is coded into a binary bit-stream and transmitted to the application end. Its bpp is calculated as the bit-stream size (in bits) divided by the image resolution. The bpp for both the sRGB and RAW reference images is computed in the same way, using each file’s size (in bits) divided by its resolution.\n\n Q8. Conclude the limitations of this work\n\nAns.: Thanks a lot for your kind suggestion. We ‘d like to conclude our limitations as follows: First, as mentioned in Sec. 3.4, our RID dataset is an ongoing project. Inspired by your suggestions, we believe it is necessary to expand the dataset by incorporating images captured from different cameras and smartphones, and to further investigate the variation in RAW metadata requirements across different ISP pipelines. Second, our MV-IR module is currently optimized for object detection, and its effectiveness for other machine vision tasks (e.g., semantic segmentation) remains unexplored. Creating corresponding annotations and either tailoring the module for specific tasks or developing a generalized version are valuable directions for future work. Third, inspired by your comments, we find it interesting to investigate the differences in required metadata between machine vision needs and human perception–oriented needs. Furthermore, designing a hierarchical structure to represent both machine- and perception-tailored metadata, and incorporating a scalable compression strategy to fulfill both requirements, would be an interesting and valuable direction for extending our current work.\n\nQ9. Minor issues related to typos and missing references\n\nAns.: Finally, we would like to express our deepest gratitude for your comprehensive and responsible review, which has greatly helped improve the quality of our manuscript. We will revise the relevant sections accordingly, incorporate the necessary references, and conduct thorough proofreading to avoid such issues."}, {"review_id": "i2aXnSDaRc", "reviewer": "Reviewer_aGYu", "summary": "This paper introduces a novel paradigm for low-light object detection called Compact RAW Metadata-guided Image Refinement (CRM-IR). The core idea is to leverage information from RAW camera sensor data without incurring the high storage and transmission costs typically associated with it. Instead of processing the full RAW image, the proposed method extracts a small amount of essential \"metadata\" from the RAW data. This metadata is then used by a lightweight module to refine a standard, compressed sRGB image, making it better suited for machine vision tasks.", "questions": "- What is the computational overhead? Could you please provide details on the increase in inference time (or decrease in FPS) when adding the CRM-IR modules to the baseline object detectors? This is a critical factor for real-world deployment.\n- The method learns to refine an sRGB image based on metadata from its corresponding RAW version. Since the sRGB image is the output of a specific, often proprietary, in-camera Image Signal Processor (ISP), how well does the trained model generalize to images from different cameras with different ISPs? Have you performed any experiments to test this robustness?\n-  And also above weakness", "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: The proposed solution is highly practical as it circumvents the high costs of transmitting and storing RAW images, which is a major bottleneck for many real-world applications, especially in edge-to-cloud scenarios. The plug-and-play nature of the proposed module makes it easily integrable into existing systems without major architectural changes.\n\nThe work suffers from several significant weaknesses that should be addressed:\n\n1. The Contribution of the Proposed RID Dataset is Limited. The authors introduce the Raw in Dark (RID) dataset for cross-dataset generalization validation. However, the overall contribution and novelty of this dataset appear to be limited considering existing LOD . The RID dataset follows the existing paradigm of paired RAW and sRGB images, which is structurally identical to the benchmark LOD dataset. It does not introduce a new format or annotation methodology that would address fundamental challenges in the field. With only 500 paired samples, the dataset is considerably smaller than the LOD benchmark, which contains 2,230 pairs. Although the dataset focuses on more extreme low-light scenes and expands the number of object categories, this represents an incremental addition to existing resources rather than a foundational or groundbreaking contribution.\n\n2. A critical weakness is the paper's failure to address the method's generalization capability across heterogeneous camera sensors, especially those with different Color Filter Arrays (CFAs). The method operates directly on RAW data, and all experiments were conducted on two Canon camera models that use a standard Bayer (RGGB) CFA. It is highly probable that the model is learning priors that are specific to this RGGB pattern. The paper provides no discussion or evaluation of how the model would perform on RAW data from sensors with different CFAs, such as RYYB or X-Trans, which are common in smartphones and other camera systems. A model trained on RGGB data would likely face a severe performance degradation when applied to a different RAW structure due to the fundamental mismatch in color channel information and spatial layout. This unaddressed dependency on specific hardware severely limits the method's claimed \"plug-and-play\" applicability.\n\n3. The paper's evaluation against the \"enhancement-then-detection\" paradigm is insufficient, as the selection of baseline methods is too narrow. The authors only select three sRGB enhancement algorithms (Zero-DCE, KinD, and YOLA) to form the two-stage baseline. The field of Low-Light Image Enhancement contains many high-performing algorithms that could form a much more competitive baseline, such as the pioneering RAW-based enhancer SID (which is cited but not used for comparison) or more recent state-of-the-art models like Rawformer. By failing to compare against a more comprehensive and challenging set of SOTA enhancement methods, the paper's claim that its end-to-end approach is superior to two-stage pipelines is not robustly supported. It is possible that a pipeline combining a top-tier LLE algorithm with a detector could achieve comparable or even better performance.", "was_revised": true, "final_justification": "The authors partially addressed my concerns, but it doesn't fully validate the accuracy of the detection. However, the article's contribution to the dataset and innovative approach can be considered a significant contribution.", "rebuttal": "First of all, thank you very much for your professional and thoughtful review. We will do our best to incorporate your suggestions to further improve the quality of our manuscript. Regarding your concerns, we hope the following point-by-point responses adequately address them.\n\nQ1. Concerns regarding the contribution of the constructed dataset\n\nAns.: Thank you for your comments. First, we would like to emphasize that the core contribution of our work lies in efficiently extracting the minimal yet essential RAW information required by downstream machine vision tasks, with careful consideration of transmission overhead. The dataset serves as just one component supporting this broader objective.\n\nRegarding the dataset, our primary motivation was to create a resource for cross-dataset validation, which is crucial not only for our method but for all RAW-based object detection approaches. To the best of our knowledge, at the time of submission, only one other real-night RAW dataset with three overlapping object categories existed—ROD (*Toward RAW Object Detection : A New Benchmark and A New Model*, CVPR 2023). However, it only contains RAW images with corresponding annotations (no sRGB counterpart), preventing it from rigorous cross dataset evaluation. In this context, our constructed dataset fills an immediate gap and provides a meaningful contribution to the low light research community.\n\nQ2. Provides discussion or evaluation on RAW data from sensors with different CFAs\n\nAns.: Thanks for your constructive and insightful suggestions. First, we agree that camera-specific details, such as the CFA pattern, are indispensable for fidelity-oriented RAW reconstruction tasks. However, our objective is different: we extract only the metadata that downstream machine vision models require but cannot obtain from sRGB images, and we deliberately omit fidelity or perceptual supervision to minimize the amount of RAW information that needs to be transmitted. Accordingly, the MV IR module applies an inverse gamma step followed by a learnable remapping to recover the information suppressed by gamma correction and compression, which depend only minimally on the camera model. In this way, our method achieves satisfactory performance with little to no reliance on the full set of camera-dependent parameters. \n\nHowever, at the current stage, we are not able to obtain such an open source dataset to conduct a rigorous cross dataset evaluation, and we appreciate your further suggestions.\n\nMoreover, we sincerely appreciate your constructive suggestions. As noted in our paper, the construction of the RID dataset is an ongoing project, and we plan to incorporate additional subsets captured with diverse smartphones (e.g., HUAWEI and iPhone) to further contribute to the broader machine vision community. \n\n\nQ3. Comparing with other RAW-based enhancement methods.\n\nAns.: Thanks a lot for your suggestions. First, we acknowledge that recent RAW-based methods have demonstrated overwhelming advantages over sRGB-based approaches; we will incorporate more RAW-based methods in the related-works section and experiments of our manuscript. In fact, we have already included the cutting-edged RAW-based enhancement method RAOD (CVPR 2023) which is particular designed for object detection tasks, and our approach achieves comparable performance while offering unique advantages in terms of transmission overhead. \n\nMeanwhile, we would like to emphasize that in comparisons with RAOD and other RAW-based methods, our focus is not solely on achieving higher performance, which is unrealistic given that those approaches have full access to the entire RAW data. Our primary objective is to minimize the transmission and storage requirements of RAW-based pipelines, enabling broader deployment in edge-to-cloud scenarios. Therefore, evaluating methods based solely on downstream accuracy constitutes an unfair comparison, especially considering that RAW images typically require 48 bpp for storage and transmission.\n\nFollowing your suggestion, we have added two RAW-based methods, AdaptiveISP (*Learning an Adaptive Image Signal Processor for Object Detection*，NeurIPS2024) and SID (*Learning to See in the Dark*, CVPR 2018). Due to time and computational constraints, we are only able to report results on the YOLOv3 backbone. \n\n| | mAP | mAP50 | mAP75 | \n|---|---|---|---|\n| YOLOv3 | 40.00 | 67.67 |43.44  | \n| SID | 40.01| 68.69 |42.84  | \n| AdaptiveISP | 41.91 | 68.55 | 45.66 | \n| Ours | 42.14 | 69.11 | 46.02 | \n\nHowever, quite surprisingly, our method still outperforms these newly incorporated RAW based methods, which can be mainly attributed to the analytical design of our MV-IR module. It establishes a clear and efficient restoration path built upon prior knowledge of the ISP pipeline.Note that **RAWformer outputs RAW images**, which would require substantial modifications to downstream detectors; therefore, we do not include it in this comparison.We appreciate your understanding and will include more comprehensive comparisons with cutting-edge RAW-based methods in the revised manuscript.\n\n\n\nQ4. Compare the computational overhead and inference time.\n\nAns.: Thanks for your valuable and constructive suggestions.  It is indeed important and necessary to analyze the running speed and computational overhead. \nAs our method is mainly proposed for reducing the transmission cost in edge-to-cloud scenarios, where the source images (sRGB or RAW) also need to be compressed and transmitted, we benchmark both stages separately for a fair assessment of runtime and computational overhead. The resulting speed and computational-cost statistics are reported below.\n\n1)Imaging end: We compare the CMCE encoder with extra two image codecs (Encoder part only): TIC (*Transformer-based Image Compression*, DCC 2022), and MLIC++ (*Linear-Complexity Multi-Reference Entropy Modelling*, arXiv 2023).; \n\n| | Para. (M) | Enc. Latency (ms) | GFLOPs |\n|---|---|---|---|\n| TIC | 12.74 | 3009.13 |338.59  |\n| MLIC++ |68.80  | 181.97 | 147.70 |\n| Ours (CMCE) | 1.78 | 40.72 | 38.17 |\n\n2)Application end: we evaluate the MV-IR module against the five enhancement methods already employed in our manuscript. \n\n| | Para. (M) | Inf. Time (ms) | GFLOPs |\n|---|---|---|---|\n| Zero-DCE | 0.08 | 6.50 | 20.76 |\n| KinD | 8.02 | 5.23 | 258.91 |\n| YOLA | 0.01 | 2.00 | 2.32 |\n| RAOD | 0.07| 1.75 | 0.21 |\n| Ours (MV-IR) | 0.08 | 2.52 | 0.27 |"}]

Author rebuttal:

Meta-review: This paper proposes a new RAW-based machine vision framework called Compact RAW Metadata-guided Image Refinement (CRM-IR). In particular, the paper proposes a Machine Vision-oriented Image Refinement (MV-IR) module that refines sRGB images into a format suitable for machine vision based on learned RAW metadata.


The proposed method does not require any changes to ISP or downstream detection model architectures, enabling easy integration into existing vision pipelines. Additionally, by transmitting only compact metadata rather than full-resolution RAW images, it significantly reduces RAW data storage and transmission costs while maintaining equivalent object detection accuracy.


Reviewers pointed out that the fundamental differences from traditional methods are unclear. They also noted that the methodological positioning, effectiveness, and utilization of cross-modal redundancy are unclear. Additionally, the effectiveness of the method for recent detectors has not been sufficiently evaluated. A detailed analysis of execution time and computational overhead is lacking. Furthermore, ablation experiments including YOLA and RAOD have not been conducted to ensure a fair comparison. Finally, the research contribution of the constructed dataset has not been sufficiently demonstrated.


The authors appropriately addressed the concerns, and all reviewers are satisfied with their responses. The paper received positive scores from all reviewers. Therefore, the AC believes that this paper provides the NeurIPS community as a whole with useful information. However, the AC strongly recommends incorporating the reviewers' comments into the camera-ready version.
Decision: Accept (poster)