Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
Authors: Gongzhe Li
Linwei Qiu
Peibei Cao
Fengying Xie
Xiangyang Ji
Qilin Sun
Venue: NeurIPS 2025 poster ()
Keywords: Tone Mapping
HDR RAW Detection
Real-time
Embedded System
Primary area: applications

Abstract: High dynamic range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particularly in autonomous driving. However, most neural networks for embedded vision are trained on low dynamic range (LDR) inputs and suffer substantial performance degradation when handling high-bit-depth HDR images due to the challenges posed by extreme dynamic ranges.
In this paper, we propose a novel tone mapping method that not only bridges the gap between HDR RAW inputs and the LDR sRGB requirements of detection networks but also achieves end-to-end optimization with the downstream tasks.
Instead of relying on traditional image signal processing (ISP) pipeline, we introduce neural photometric calibration to regularize dynamic ranges and a scaling-invariant local tone mapping module to preserve image details.
In addition, our architecture also supports performance transfer finetuning, enabling efficient adaptation from the LDR model to the HDR RAW model with minimal cost. The proposed method outperforms traditional tone mapping algorithms and advanced AI-ISP methods in challenging automotive HDR scenes. Moreover, our pipeline achieves real-time processing of 4K high-bit-depth HDR inputs on the Nvidia Jetson platform.

Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
Gongzhe Li
Linwei Qiu² Peibei Cao
Fengying Xie² Xiangyang Ji Qilin Sun
1 School of Data Science, The Chinese University of Hong Kong, Shenzhen, China Tianmussan Laboratory, Beihang University, Hangzhou, China Schooo of Artificial Intelligence, Nanjing University of Information Science and Technology, China *Department of Automation, Tsinghua University, Beijing, China SPoint Spread Technology, China gongzheli161in cuhk, edu. en, "sngqi I cucukk .nd.cch..edu.ccrr......
Abstract
High-dyyamic-range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particularly in au- tonomous driving. However, most neural networks for embedded systems are trained on low-dynamic-range (LDR) inputs and suffer substantial performance degradation when handling high-bit-depth HDR images due the challenges posed by extreme dynamic ranges. In this paper, we propose novel tone mapping method that not only bridges the gap between HDR RAW inputs and the LDR sRGB requirements of detection networks but also achieves end-to-end optimiza- tion with downstream tasks. Instead of relying on the traditional image signal processing (ISP) pipeline, we introduce neural photometric calibration to regularize dynamic ranges and scaling-invariant local tone mapping model to preserve image details. In addition, our architecture also supports performance transfer finetuning, enabling efficient adaptation from the LDR sRGB images to the HDR RAW images with minimal cost. The proposed method outperforms traditional tone mapping algorithms and advanced AI-ISP methods challenging automotive HDR scenes. Moreover, pipeline achieves real-time processing of 4K high-bit-depth HDR inputs on NVIDIA Jetson platforms.
1 Introduction
Real-world scenes can exhibit an immense dynamic range, reaching approximately 280 dB [ HDR cameras, by capturing broader range of luminance, not only enrich visual content but also substantially enhance visual perception and decision-making- uuttimayy improving navigation safety 2. State-of-the-arr HDR sensors, such as the SONY IMX490 [ deliver HDR RAW streams with an impressive dynamic range of to 140 dB and preserve unparalleled levels of unprocessed detail. As a result, computer vision systems must handle challenging high-bit-depth HDR (e.g., 24-bit) scenes and rapidly changing lighting conditions in real-time, such transitions when entering or exiting a tunnel.
Existing computer vision methods, particularly DNN-based object detection networks 4 6 С are designed for www-dynamic-range (LDR) inputs processed through the traditional image signal processor (ISP). When applied directly to high-bit-depth HDR imagery, these systems suffer significant performance degradation 3.1 due to extreme luminance contrasts, leading to the collapse of feature extraction in neural networks. A common solution to this problem is to utilize professional HDR ISP, such as those described in 19 I0 11. to convert HDR RAW images into LDR sRGB equivalents through a series of image processing steps. A critical component of these ISP is tone
39th Conference Neural Information Processing Systems (NeurIPS 2025).
"Corresponing author.

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mapping 12 13 14. which compresses the dynamic range while preserving much detail as possible.
Handcrafted tone mapping approaches remain fundamentally optimized for human visual perception rather than machine vision tasks, often resulting in suboptimal performance when integrated with modern computer vision architectures. This mismatch also extends to traditional ISP pipelines, which inherit similar perceptual optimization constraints. Recent DNN-based AI-ISP methods
19 20 largely follow traditional ISP pipelines for processing HDR RAW inputs. Some works [5 [6] implement neural approximations of key modules (e.g.. AWB, Tone Mapping.
[1 16
17 18
CCM) for task uusk-specific optimization, while others employ simplified tone curve parameterizations
22 22 or proxy- ISP tuning [88 [9] [7] guided by the downstream task. However, these methods remain constrained by ISP pipelines, which include redundant components and limit their adaptability to diverse HDR scenes in computer vision applications.
Modern edge computing platforms (e.g.. NVIDIA Jetson) and neural network in- ference engines are exclusively optimized for LDR sRGB inputs. However, the ex- treme 24-bit dynamic range of HDR RAW images presents significant challenges for hardware- hardware-friendly operators, such as piece- wise linear functions [23] or perceptual tone curves [3] which fail to represent the full dynamic range adequately. This intro- duces gap between the HDR RAW inputs and these engines, necessitating innovative bridging solutions that can be effectively run on embedded systems.
mAP v.s. Parameters Latency
50
Oun
CLAIEE
Our
RAGDNe223
IDR
Mantukus
mAPpys
Recodig5521
TANe222
AmsonmbeNa22
25
Zero 2CE+
80522
Rod Time (G3EPS)
10'
Inference Lateney(ms)
To bridge the gap between high-bit -igh-bit-depth HDR RAW inputs and LDR sRGB re- quirements of neural networks, we propose a lightweight and efficient tone mapping framework specifically optimized for HDR RAW object detection. The proposed archi- tecture enables efficient computation and low latency, making it well-suited for edge platforms. To address changing lighting conditions in real-world environments, propose neural HDR photometric calibration to dynamically regularize extreme dynamic ranges, normalize diverse radiance distributions into unified scale, and enhance cross-scene generalization and detection performance. Our method outperforms both handcrafted tone mapping algorithms and modern AI-ISP baselines in autonomous driving datasets. Comprehensive ablation experiments demonstrate that our method significantly improves the detection performance on HDR RAW inputs. Furthermore, show that our approach can process large-resolution HDR RAW data in real-time, achieving balance between detection performance and inference latency. In summary, we make the following contributions:
Figure 1: Comparison of detection performance and model complexity on the Rol dataset using Faster R-CNN (1280 x 1280 resolution). The symbol * indi- cates the performance of our Ours (Lite) model with 4K resolution input, achieving 45 FPS on NVIDIA Jetson platforms (16-bit float precision).
We propose a novel tone mapping framework for vision tasks to bridge the gap between high- bit-depth HDR RAW data and LDR sRGB input requirements for HDR object detection, specifically tailored for embedded platforms.
We introduce a neural HDR photometric calibration method that dynamically regularizes extreme dynamic ranges through sppiacee-ppcce unification.
We design a lightweight local tone mapping model based on a scaling- invariant principle, enhancing detection performance while reducing runtime.
We validate our method on an end-to-end object detection task, demonstrating its ability to efficiently process HDR RAW data while balancing detection performance and computa- tional efficiency. Experimental results show that our method enables real-time processing of 4K HDR RAW videos.

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Pipeiino
Otjjctt Detector
Conveniional Imaging
Deleciin
Spin Pooel Captires
Neural Pastendry Calbezion
Grodlest Proparation
Naral Phatoam: Gilranic
Scaling-maram Tone Mapper
r
Objjct Detester
lalige Teee Mapso
Parameter Upsorn
8-
Deleciun Roula
Ox
Σ
(Cre. Laa)
Detoction
Figure 2: Conventional HDR RAW processing and detection are typically treated as separate tasks and optimized independently. We propose alternative pipeline for HDR object detection, where tone mapping is optimized specifically for detection. The proposed pipeline regularizes the dynamic range and utilizes scaling-invariant tone mapper process both tasks simultaneously.
2
Related Works
2.1 High- DynamDD Range Imaging
HDR imaging techniques aim capture a broader dynamic range of luminance. However, con- ventional CMOS sensors are limited in their luminance coverage, driving the development of HDR imaging solutions. Multi-exposure fusion (MEF) 24 25 is a widely adopted approach in the industry, leveraging weight fusion strategies to recover the dynamic range from multiple exposures. While effective photography, MEF methods are unsuitable for machine vision due exposure latency and motion artifacts. For crfityycritical applications like autonomous driving, researchers have explored single-shot HDR imaging solutions, including neural exposure control [26] 25], spatially
varying pixel exposure (27 (8), and lighting modulation devices [29] 30
which extend dynamic
range while avoiding motion artifacts.
While HDR imaging offers a broader dynamic range and richer tone reproduction, it necessitates novel methods fully leverage its potential. Recently, [15] introduced 24-bit HDR RAW image dataset captured using the IMX490 sensor, which employs advanced Split Pixel architecture [3] to capture an impressive dynamic range 140 dB (approximately 24 stops). This extreme dynamic range significantly surpasses that of existing datasets, such as LoD [31 (14-bit), PASCAL RAW [32 (14-bit), and Vision [33 (12-bit), presenting considerable challenge to current computer vision systems.
2.2 Post-Captured Tone Mapping Algorithms
Traditional computer vision networks are typically tailored for LDR images processed by the Camera ISP pipelines [10 I1 34). Tone mapping algorithms [12 14 [3 critical component of camera ISP, compressing dynamic range and equalizing luminance to render HDR content into visually pleasing LDR images. However, these algorithms and ISP systems are primarily designed for human perception, often including unnecessary steps and being computationally expensive. While effective for visual aesthetics, they are suboptimal for machine vision tasks such as object detection. To address these limitations, DNN-based AI-ISP approaches [15] [6] [7] introduce learnable ISP pipelines that leverage neural networks for HDR tone mapping. These methods can be jointly optimized with downstream neural networks via gradient propagation, outperforming traditional ISP systems in vision tasks. Additionally, differentiable approximations [99 techniques such neural architecture search [19] or parameter mapping [18] to simulate real ISP systems. Unsupervised low-light enhancement methods 22 also act as independent tone mappers, equalizing luminance through tone curves. While these methods reduce computational costs, their reliance on specific models limits generalization under varying illumination conditions. contrast, method employs HDR photometric calibration and efficient tone mapping network tailored
18 I7] model hardware ISP using

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Ours
HDR ISP
CLAHE94
RAODNe223
ZeroDCE++21
ReconfigSSP21
Figure 3: Visual comparison of different methods HDR RAW images. The first illustrates daytime scenes, while the second row showcases night scenes. Our method demonstrates superior performance compared the other methods. Zoom in see details.
for HDR perception, climinating dependence on ISP meta-architectures and avoiding the need for complex design.
2.3 Perception Networks on Embedded Systems
Practical vision applications, such as those in automotive and robotics, are often deployed on embed- ded systems (c.g., NVIDIA Jetson, ARM Core) with limited computational resources, necessitating optimized neural network inference. Lightweight architectures edge platforms, enabling efficient inference under resource constraints. To further enhance perfor- mance. quantization techniques convert floating saaiing-pointt weights into low-precision integers [36 37], while pruning methods [38] 39) eliminate redundant layers to reduce memory and computation overhead. These optimizations make neural networks more suitable for embedded systems, but often overlook the challenges posed by HDR RAW inputs.
[ 35
are specifically tailored for Al
Most deployed models trained LDR sRGB images (e.g., 8-bit), making them incompatible with HDR RAW data (c.g., 24-bit). This mismatch can result in ineffective feature extraction and
degraded performance for HDR perception tasks [15 40 33) To address this issue, we propose novel tone mapping method that is jointly optimized with the detector, enabling efficient and accurate processing of HDR RAW data on embedded systems. Our method bridges the gap between HDR imaging and embedded perception, enabling resource- resource-constrained platforms to handle HDR tasks effectively.
3 Method
In this section, we first analyze the impact of dynamic range detection performance and propose neural photometric calibration method to regularize it. Additionally, we present a scale-invarian tone mapping approach designed to optimize image details, guided by the requirements of the downstream detector. An overview of the proposed method is illustrated in Fig
3.1 Dynamic Range Regularization
Dynamic Range and Detection Performance Analysis. Although 24-bit HDR RAW images retain rich details that could theoretically enhance detection performance, experiments reveal counterintuitive issue: directly applying detectors to HDR images causes gradient instability (see T. We explore the relationship between image dynamic range and gradient propagation neural networks. Specifically, Model (GMM) [I] to approximate the histogram across the entire dynamic range. The distribution p(x:) of an image is explicitly expressed weighted mixture of N Gaussian component densities N (pr, os), designed to approximate the pixel histogram the HDR input:
model pixel distributions of HDR image using Gaussian Mixture
N
p(x)
Wi N(
(I)

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where wi are the weights, and Hj and o; are the mean and standard deviation of the i-th Gaussian component. N is the number of components. Next, propose minimal model consisting of single-layer CNN with MSE loss to analyze the effects of dynamic range on gradient propagation dynamics. This simplified model allows for the theoretical derivation the gradient relationship:
ac Ow
(w u w -0) BBx)) B(x²)
Σ
Wr( + tof + Mo)
d:
N/ + 0:
dw
pi 0i
(2)
N
de + di
di
di +1 d:
o +
0i
1--1
+
0j
x
Ws
Here, the single-layer CNN is expressed as + C, where w represents kernel weights, is the input, e is bias, denotes the convolution operator. L is the MSE loss and ý denotes the ground truth label. de represents the ratio of the maximum value to the minimum value in single Gaussian component, approximating the dynamic range. As shown in Eq. (2), the gradient is proportional to the square of the dynamic range, indicating that HDR images lead to higher gradient fluctuations compared to LDR images. This fluctuation makes it challenging for neural networks to extract effective features, leading to instability in gradient propagation provided in the supplemental material.
Detailed proofs are
HDR Photometric Calibration. The gradient fluctuations in HDR images motivate us to regularize their dynamic range to stabilize training. HDR photometric calibration is commonly used to normalize pixel values to the irradiance of the real-world scene, projecting HDR images into unified radiance space while preserving fine details. Previous studies 43 44 45) have shown that HDR photometric calibration enhances detail reproduction and improves visual perception. The HDR photometric calibration process can be expressed as:
I-V.R.
where I represents the calibrated image, R denotes the scene radiance, and V models the optical effects. This calibration is typically modeled as a linear transformation.
3.2 Neural Photometric Calibration
In practice, measuring real photometry for HDR image calibration is challenging. To address this issue and adapt to different lighting conditions, we need to make educated guesses about the minimum and maximum radiance values in the original scene. Hence, we propose Neural Photometric Calibration, a novel method designed to approximate Eq. (3) and estimate its adaptive parameters through end-to-end optimization. Specifically, we define learnable transformation function as:
I (K b) +
where I is calibrated HDR RAW image, R is captured scene radiance. K is the scale map, and b is bias term. To handle complex scene illumination, we introduce linear interpolation-bate modification to adaptively predict the scale map:
(5) Here, K is controlled by scene key radiance [13 for both day and night scenes (c.g. Ray and RNight), ensuring compatibility across different lighting scenes. E is identity matrix, and a is a weight map constrained by sigmoid function:
K = a RDaY + (E-x) RNight
sigmoid (FE (R+))
(6)
where the uncalibrated image R is first downsampled (L) to low resolution (LR) and then passed through a feature extractor FE to generate the weight map
The radiance in sunlight can be much million times more intense than at night (Ryay R This numerical disparity introduces instability when directly predicting K using the convo- lution layer in Eq. (5) Thus, we enforce the convolution layer to predict log scale S == log K:
K =100),, S S= a log R + (E ax) - log Rright
The logarithmic mapping allows us regress compact values, where 100 is resolved to the radiance scale, then the scale map approximately at 107 cd/ /m2 and 10'cd/m² /m2, respectively, as cited from .. and the bias term = 10 in our experiments.
is upsampled (1) to the original size. In this paper, we set Raay and RNight
5

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TM
y WX
LDR Pixel Intensity,
1.0-
04
0.0
022
0.6
088
HDR Pixel Intensity, x
Linear least squires fitting of pixel intensities between
(b) Visual Comparison between TMg and TM.
LDR HDR images.
Figure 4: (a) Linear least squares fitting on pixel intensity from processed LDR image and input HDR images. TM exhibits large bias gain, while TMst only introduces scale gain. (b) The comparison of the LDR images shows that TM3ı produces smooth regions and effectively enhances object visibility, while TM often results in background noise.
3.3 Scaling-Invaree Invariant Tone Mapper
We propose lightweight and effective local tone mapper that leverages the seale-invariance property of neural networks. We first prove that a bias-free neural network [46] (i.e. no additive bias terms) with L layers, composed of convolution weights { ReLU activation, and Batch Normalization,
invariant:
fsi(ax) RELU BN K2
RELU BN1 K1 (QT)
KK +
111
RELU BN K2
RELU
01
K1 # 141
(8)
RELU BNL K2 ...... ....RE
?1
01
= a: RELU O BN, K2 *... ..... o BN, K1 #
= a- fsi()..
where denotes the cascading of network layers, * is convolution operator, and BN denotes batch normalization, defined as BN(x) = 7 (x- -p/aa, where 7, J4, a are statistic parameters. Since convolution layers rely local dependencies, this property inherently preserves local relationships. This design ensures that the ocaling-invariant network functions as local tone mapper without requiring additional modifications. Scale-invariane is critical property for tone mapping, ensures consistent reproduction across varying dynamic ranges.
The input HDR RAW image processed using scale-invariant tone mapper (TMsi), and the resulting LDR image is fed into the object detector (OD) to obtain object information. This operation is formally expressed
c, OD(TMsr(I)
where the d are the detected bounding boxes and and the corresponding inferred classes and confidence scores. And in the following text, we will refer to the scaling-invarian invariant tone mapper as TMsT and the scaling-variam variant tone mapper as TM.
Architecture Details. The detailed implementation is illustrated in Fig. 2 The neural photometric calibration module consists 3-layers Conv-ReLU followed by a sigmoid activation. The tone mapping model TMşt consists of 4 layers of Conv-BN-Red BN---LUU each with 32 channels. To preserve the invariant property, all bias terms are removed from the network. Additionally, we introduce lightweight variant with 16 channels, referred to Ours (Lite), to enable efficient inference for large-rese largo-resolution inputs on edge devices.
3.4 End-to-End Optimization
Tone Mapper Pretraining. In our method, the input HDR RAW images are processed to compress dynamic range before passing them for end-to-end optimization. To pretrain the tone mapper, we use the Normalized Laplacian Pyramid Distance (NIPD), a self-supervised loss function commonly applied perceptual image quality optimization 53 43.. The NLPD loss compresses the dynamic range while preserving tone details
detector, making effective weight initialization critical

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Table I: Quantitative comparison of basic detectors (Faster R-CNN and YOLOv3) the ROD dataset, evaluated using mAP, mAR, AP50, and AP75 metrics. The best results are highlighted in bold, while the second-best results are indicated with an underline. NAN indicates that the results do not converge.
Methods
Group
Faster R-CNN
YOLOv3
S
Larency(ms)
Fl.
mAP
AP30
AP75
mAR
mAP
AP50
AP75
mAR
HDR RAW
Direct Input
NAN
NAN
HDR ISP
Handerafied
45.7
69.5
50.1
54.9
42.2
68.1
45.6
50.5
Mantick08
P
Handerañedd
45.6
69.1
49.7
53.3
44.8
71.0
49.1
52.8
CLAHE94
Mapping
68.2
43.0
51.4
R
19
End-to-Ead ISP
41.4
63.2
45.6
49.7
40.9
60.0
44.0
48.4
92.7
455.14
Fe10-1XCF4421
11
Low-Light Echance
28.6
47.4
30.2
38.5
27.5
49.0
27.5 5
36.3
29.8
16.51
SC122 2H
26.1
43.7
27.0
35.6
25.1
45.6
25.1
33.5
64.3
283.14
1es2
26.8
43.6
27.5
37.8
22.0
41.5
23.1
30.4
123.3
382.04
Ne [6
AL-ISPNet
37.6
59.5
47.0
33.1
56.4
34.4
41.9
31.3
0.96
RAODNe223 [5]
46.1
69.4
50.6
55.4
42.9
65.8
46.5
50.9
41.2
0.81
RawOCCookee223 42
34,8
55.3
34.4
44.4
28.2
46.3
28.4
32.9
55.1
12.89
Ours .ite
End-to-Fnd
47,8
21.7
52.9
56.9
42.6
68.5
46.6
51.3
8.8
23.40
Ours
Tone Mapping
49.8
73,3
55.6
58.7
45.1
71.4
50.2
53.0
25.4
62.11
Table 2: Performance comparison using per- Table 3: Impact of image texture metric and detec- formance transfer strategy on RoD dataset. tion results.
Pretrained Weights
Image S1 Contrast(
Entropy [2 of GLCM(1)
Methods
Methods
mAP
R4WAdapeer24
HDR RAW
0.00
0.00
-NAN-
COCO
AdaptivelSP24
1r
22.5
33,5
SC122
22
0.18
19.17
26.
Ours
30.6
47.5
Zero-DCE++2
0.18
20.07
28.6
RAWAdapeer24
49
15.7
IANe222 16
0.19
21.92
37.6
Object365 [50
AdaptiveIS224
17
24.7
36.1
RAODNe123
15
0.22
24.32
46.1
Ours
38.8
50.7
Ours
24.63
and effectively enhances local contrast, which we observed to be beneficial for improving object detection performance.
Performance Transfer Strategy. Our method can also act as domain adapter 49
17 transfer
performance from the LDR sRGB domain to the HDR RAW domain. Through the performance transfer strategy, can achieve high performance with only few training sources by utilizing pretrained weights on LDR sRGB. In this setting, we optimize only the tone mapper and the detection head, while keeping the remaining components frozen.
Loss Function. In our experiments, training is guided by detection losses commonly used object detection pipelines [4
Liotal = Lobj. + Lelass.
(10)
Here, the total loss Liotal consists of a location regression loss (Cobj.) ) and a classification loss (Celass.), both of which are widely applicable in detection tasks.
4 Experiments
4.1 Experimental Setups
Dataset. We evaluate our method on the RoD dataset [15], which contains 20,089 24- HDR RAW images. Unlike RAODNet [15), we test our proposed method on mixed scenes to validate its effectiveness and generality. Note that the published dataset is more challenging than in the paper.
Implementation Details. We employ two widely used object detectors: Faster R-CNN (ResNet50)
and YOLOv3 (DarkNet53) [5]. Our implementation based on the MMDetection [54] codebase. Following the setup in 15). HDR RAW images are processed with linear demosaicing [55] 56 to
restore color channels and resized 1280 x 1280 size. During training, we apply random flipping for data augmentation, use batch size of 8, and train for 14 epochs with an initial learning rate of
le-2. The learning rate is decayed by a factor of 10 at epochs and And we use Faster R-CNN
4 as the main detector for the following ablation studies. To accelerate adaptation to the HDR RAW domain, we initialize the model with COCO 48) pretrained weights.
Evaluation. We evaluate performance using mean Average Precision (mAP) and mean Average
7

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Table 4: Performance comparison under different scenes of Faster R-CNN.
HDR ISP
IANe21
RAODNe123
Neural Calibration
Neural Calibration
Scenes
[T]]
16
[S]
w/o
Day
32.0
43.4
35.9
36.7
40.3
Night
37.9
31.6
45.6
38.5
45.8
Mixed Scene
45.7
37.6
46.1
40.7
49.8
Table 5: Performance comparison using per- formance transfer strategy on RoD dataset.
Table 6: Impact of texture metric and detec-
tion results on RoD dataset.
Scaling
RoD[T3]
RoD + RhoVision
88
Ditectors
Methork
mAP
APSO
AP75
mAR
Invariant
MAP
AP75
AP50
mAP
AP75
AP50
Spazso
HDR ISP T0
45.0
08.7
49,3
61.0
RCNN 7
Ours
51.3 (19.)
74.7
$7.1
66.4
49.0
72.5
52.4
14.3
24.5
13.5
Defermable DETR
HDR ISP 10
50.2
73.6
57.5
64.7
49.8
73.3
55.6
26.5
55.9
27.9
(440
76.4
63,4
68.2
Recall (mAR) across all Intersection over Union (IoU) thresholds, along with Average Precision (AP) IoU thresholds of 0.5 (AP50) and 0.75 (AP75). Additionally, we test model complexity in terms of the parameters (K), computational complexity (FLOPs), and inference latency (ms) on NVIDIA Jetson platforms.
4.2 Comparison Experiments
Quantitative Comparison. We compare the proposed method with state-of-the-aar (SOTA) methods for HDR object detection; the quantitative results are presented in Table [ We categorize these comparison methods into five groups: Handcrafted ISP [10), Handerafted Tone Mapping [ 14), End-to-End ISP
[5 42), and End-to- End Tone Mapping (our methods). The HDR ISP pipeline is adapted from the post-processing of HDRPlus 9 10), a professional ISP pipeline designed for HDR image rendering. Directly using HDR RAW images input for object detectors results in NaN outcomes due their extreme dynamic range, where most pixels tend toward small values. This prevents the neural network from extracting sufficient features, highlighting the current models' inability to effectively capture meaningful information. In contrast, tone mapping algorithms such as Mantiuk08 [12] and CLAHE94
[9). Low-Light Enhancement 121 (22), AI-ISPNet [18] [6
14 compress the dynamic range while preserving image details, resulting in performance that is comparable to SOTA and significantly benefits the detection task. Differentiable ISP methods, such as AnscombeNet21 [8] model ISP transformations latent space but lack specific tone mapping operations, resulting in lower performance. ReconfiglSP21 [19] employs proxy optimization to fine-tune a simple ISP system, achieving results comparable to HDR ISP pipelines. Unsupervised low-light enhancement [2 22 achieves lower performance because it cannot effectively handle high dynamic range scenes, and the processed image still dark. As for AI-ISP methods [16] [5] 42),
IANet22 [16] employs piece-wise linear curve as tone mapper, which is only suitable for monotone lighting scenes, resulting in low performance. RAODNet23 (15) uses both global and local tone curves to compress the dynamic range, achieving results comparable to state-of-the- art methods. RAWorCooked23 [42] introduces learnable contrast correction function, but struggles with handling extreme dynamic ranges. Our method outperforms all comparison methods, achieving improvements of 3.7% in Faster R-CNN and 1.6% in YOLOv3 compared to the second- best performance, respectively. Additionally, it shows 4.1 % improvement compared the HDR ISP pipeline. These comparison results demonstrate that our proposed method effectively explores potential information in HDR RAW images, leading improved detection performance.
Qualitative Comparison. In Fig. 3 we compare our method with the baseline by visualizing detection results across both day and night scenes the RoD dataset. For daytime scenes, our method enhances regional dynamics and highlights potential objects, making detection easier. In night scenes, adjusts the contrast across regions, making candidate areas more prominent. Our method can handle varying lighting conditions, thanks to neural photometric calibration. This process normalizes the radiance space and optimizes tone mapping for the downstream detector, offering an optimal solution for machine vision perception. More visual comparisons are provided in the supplemental material. Inference Latency Comparison. We evaluate the parameters and inference latency of comparison methods in Fig. Our method utilizes a plain feedforward architecture, where each layer takes the output of its preceding layer as input and passes its output to the following layer. This design ensures favorable accuracy-speed trade-off, even though it does not use the fewest number of parameters.
8

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Restkrtee
IMX490 HDR Camera
HIRR ISP
2002
HDR.ISP
Inference Latency (ms))
154
Ours (Lite)
RAODNet
.08
Dhirs
58-
Ours (Lite)
025
Nvidia Jetson Platform
mAP(%)
Figure 5: Left: Performance comparison on NVIDIA Jetson platforms. Right: Illustration of our hardware prototype, integrating HDR cameras and the NVIDIA Jetson platform for real-world evaluation.
reduce FLOPPs by downsampling the input resolution, they also lead to loss of image details, which can potentially harm performance. In comparison, our approach strikes the optimal balance between efficiency and performance.
Although methods such as 16 15 22
4.3 Ablation Study and Discussion.
Ablations on Performance Transfer Strategy. We evaluate the performance transfer strategy as described in Sec. 3.4 Specifically, we initialize the object detector with publicly pretrained weights 48 (50] and fine-tune only the tone mapper and detection head for 5 epochs. We also evaluate the RAW domain adapter methods 44 [7 with the results shown in Table Our approach optimizes only 33.6% of the total parameters of Faster R-CNN (tone mapper: 0.10% detection head: 33.6%), yet achieves state-of-the-arr performance compared to other methods. Our method stands out with its simple yet effective architecture, avoiding ad-hoe designs while achieving strong performance.
Alltioos on Scene Generalization. To evaluate cross-scene scene generalization, we divided the RoD dataset [15] into two subsets based on scene lighting. HDR ISP [9] is introduced as baseline due to its ability to generate consistent LDR images, and we also introduce AI-ISP methods [16 15]. The results, shown in Table 4 indicate that IANet22 [16] and RAODNet23 [15] outperform HDR ISP in a single scene. However, neither method matches HDR ISP performance across other test scenes. tone mapping module, which is only effective for monotonous lighting scenes. We also conduct an ablation study on neural photometric calibration. This module improves performance across all test scenes, demonstrating its effectiveness in radiance normalization.
These limitations arise because they rely on the PWL curve
Ablations on Ad Advanced Detector. We also evaluate advanced detectors "Sparse-RCN) 7 and Deformable DETR [8] in our method. The experimental results in Table 6 show that our method significantly improves detection performance on HDR RAW images, outperforming HDR ISP by 6.3% and 4.8%, respectively. These results demonstrate that our method exhibits improved generalization across various downstream detectors.
Analysis of Graling-Invarian Tone Mapper. To evaluate the generalization ability of our proposed cealing-invariigg tone mapper, we train the model on the RoD dataset [15] and test it on the Rho dataset 333 which contains HDR RAW images captured by the same sensor in different scenes. The results Table55show that the scaling-invarian tone mapper achieves superior generalization performance on the test dataset. Additionally, we evaluate the relationship between pixel intensities in the processed LDR image and the input HDR image using linear least squares fitting. As shown in Fig. 4 (left), the TM introduces large bias gain, with the magnitude being much larger than that of the scale gain. This generates more noise and reduces the contrast between objects and the background, which may negatively affect generalizability.
From another perspective, tone mapping is necessary to map the HDR image onto the LDR image space. This space must encompass all possible rescalings of those images, including the origin. This implies that TM(a - x) = a TM(x), Va: > To achieve this, we design a bias-free CNN, which ensures that any negative entries in the input are set to zero. Multiplying by nonnegative constant does not change the sign of the entries in vector. Therefore, the bias-free network remains scale-invariant and can rescale the image without introducing any bias.

---

Analysis of Image Texture. We evaluate image texture metrics using image contrast [1 and the entropy of the Gray-Level Co-occurrence Matrix (GLCM) [52]. As shown in Table 3 our method enhances image contrast and highlights object regions, leading to improved detection performance. These results underscore the importance of texture information for detection tasks.
4.4 Real World Evaluation.
We evaluate the latency and mAP performance of YOLOv3 [5] on the NVIDIA Jetson AGX Orin (16-bit float precision), with the results shown in Fig. (left). Our method (Lite) achieves runtime of 22 ms (45 FPS) for 4K resolution (4096 x 2160), striking an excellent balance between performance collect HDR RAW video dataset from real-world driving
and real-time inference. Additionally, scenes to validate the proposed method. Fig. (right) shows our hardware prototype for real-world evaluation, while the right side provides an example dataset used in this study.
5 Conclusion and Limitation
Real-world scenes present significant challenges for computer vision due to their wide dynamic range of luminance. Instead of relying on conventional ISP pipelines, we propose real-time, icene-adaptive tone mapping method that optimizes image details for HDR detection. Our approach introduces neural photometric calibration regularize the dynamic range, ensuring generalization across diverse lighting scenes. Additionally, a scaling-invariant tone mapping module is integrated into an end- to-end trainable vision pipeline, optimizing image details through joint training. With its efficient architecture, our method supports low-cost performance transfer strategy, enabling adaptation from the LDR sRGB domain the HDR RAW domain by finetuning only few parameters. Experiments demonstrate that the proposed method outperforms SOTA methods in both detection performance and inference latency, while also achieving real-time processing of 4K HDR RAW inputs.
Although our proposed method effectively handling a variety of lighting conditions and significantly outperforming comparison methods, it still requires cascading with detectors for joint processing of HDR RAW input. We believe that a well-designed detector can directly handle HDR data by fine- tuning certain parameters adapt HDR RAW data without any additional processing, thus further exploiting the information from sensor data and improving both the efficiency and effectiveness of detection. This is the direction of our future work.
6
Acknowledgement
This work was supported in part by the National Nature Science Fundation of China (62375233, 22772233,,,222423, Shenzhen Pivot Funding (2024TC0036), and Science and Technology Pro- gram ZDSYS2021 1021 11 14150025 We also express our sincere gratitude to Prof. Kede Ma of the City University of Hong Kong for his insightful discussions and valuable comments.
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Supplementary Material
In this file, we provide the following supplementary studies:
Details of Implemented HDR ISP pipeline appendix A
The proof of approximation to Dynamic Range appendix
The proof of scaling-invariant Tone Mapping appendix C
More Comparison Experiments.
More Ablation Studies. appendix
More Vision Comparison. appendix
Real World Evaluation. appendix F
A
HDR ISP Details
In this section, we describe the HDR ISP pipeline used in the comparison method [ This pipeline consists of a series of operations, as illustrated in Fig- 6 We follow the implementation described in [9 [0], with modifications applied to the modules preceding the tape-napping algorithms. The intermediate results of key components (indicated by the red dashed lines Fig. (6) are shown in Fig. 7 which demonstrate how the HDR data is transformed into a visually appealing LDR image after undergoing several nonlinear operations. Next, we introduce the key steps in this process.
Black-level Correction: We should subtract an offset from all pixels so that pixels receiving no light have a value of zero. This offset obtained from optically shielded pixels on the sensor.
T = I Tb
(11)
where Ts is the black level.
Anti-Aliasing filter: An anti-aliasing filter is type of low-pass filter that prevents aliasing components from being sampled.
Inaf I knaf
(12)
where kaaf is 5 x 5 filter kernel, having non-zero elements only at the corners and center. The kernel
is defined as: kanf 1/16
Auto White Balance: The AWB (Auto White Balance) module is responsible for adjusting the image to ensure that the four (RGGB) channels are linearly scaled, so that grays in the scene correspond to grays in the image. These scaling factors calculated using the Soryd white balance algorithm, which adjusts the pixel values based on the gray-world assumption. This assumption posits that the average of all color channels should produce a neutral gray image.
IR
9R
JR
Icr
0
9Gr
0
IGr
(13)
1Gw
0
9Gb
I
Ig.
0
0
0
JB.
where the parameters gR, Jar. JB are color gain, which can derived by the gray world algorithm.
Demosaic: Demosaicing converts a Bayer raw image into a full-resolution linear RGB image, preserving texture details. We use combination of techniques from the Malvar algorithm (55].
JR,G,B Demmosiic[(nn.cr,ch...)
(14)
where T (R.,G. denote the single channel bayer array, JR.G.B denote the complete 3-channels RGB image.
Local Tone Mapping: The LTM (Local Tone-Mapping) block simulates the exposure fusion algo- rithm 10 24 by brightening darker areas while ensuring that brighter content remains unsaturated.
7
Ium
l wi
(15)
14

---

HDR RAW
DPC
BLC
AAF
AWB
Demosaie
LDR
PNG Compression
EEIT
GAC
GTM
LTM
Figure 6: The key components of the HDR ISP pipeline, text for details. We visualize the results for the modules marked with a red dotted line.
where I is the synthetic exposure image, and w represents the corresponding weights calculated based on image content.
Global Tone Mapping: The GTM (Global Tone-Mapping block follows the LTM, enhancing the
overall luminance. An S-shaped contrast-enhancinn tone curve [13] is applied to the linear sRGB image. This curve is then concatenated with the sRGB color component transfer function, which transforms the image from linear sRGB to nonlinear sRGB. The Reinhard tone curves 13 can be expressed follows:
I,o
exp
Σ log (6 + +1))
(16)
N
I
P.
Igtm
(17)
1 1
where 5 a small value to avoid numerical overflow.
Gamma Correction: The GAC (gamma correction) is used to match the non-linear characteristics of a display device or human perception. We adopt the correction function Eq. (18) recommended in
ITU-R BT. 709 standard (57), which is widely used in commodity cameras today.
12.92 I,
< 0.00304,
Igamma
(18)
1.055 1 0.055, I >> 0.00304.
Edge Enhancement: The EEH (Edge Enhancement) module enhances image details and edges, improving image clarity and visual appeal. It particularly useful for accentuating finer image structures. The module can be expressed as follows:
Sharpen P. I + (1 (1-pa) Intureed
(19)
In the ISP pipelines, these modules combined in series and exccuted sequentially on the HDR RAW image to stably generate pleasing and consistent LDR sRGB images.
B The Proof of the Approximation to Dynamic Range
1. Dynamic Range Ratio for Single Gaussian Component, For Gaussian component with mean and standard deviation a, define the dynamic range:
d
p pーo K>0.
(20)
This dynamic range measures the distance of the component relative its mean.
2. Relating d to and a. Solve for μ in terms of d and a:
d --1 1
(21)
d d-1
3. Second Moment and Mean of a Single Component For a Gaussian component:
E(x?) = o² + 2 E(x) =
Thus:
E(x22) B(x) = 2 1 + p.
Substitute p = o- d+1. d-1"
E (x22) E(x) o² + (a d+1 1
d ++1
+
(22)
1-1
15

---

Demosaic (Gamma for visualize)
Local Tone Mapping
Global Tone Mapping
Gamma Correction
Figure 7: Visual comparison of key steps in the HDR ISP pipeline.
Simplify:
1+ (d + 1)2 (d 1)
d + +
B(x²) + E(x) = o²
(23)
d - 1
Bounding Exx²) + B(x) using Expand the squared term
(d + 1)2
d + 2d
4d
+
(24)
(d - 1)2
1 2d
+1
(d
- 1)2"
Substitute back
4d
d 4+1 1 d-1
E(x²) + B(x) = 0²
2
+
(25)
(d
1)
Inequality Analysis Using the AM-GM (Arithmetic and Geometric Means) Inequality:
p + 0 2
Vua
# 2 2/1oo
(26)
For the dynamic range d:
+ 0 2 2/10
d
(27)
This implies that d grows as the overlap between μ and a increases, amplifying BBx?) + B(x).
6. For a GMM with K components:
E(x²) +((()) Σ (o + 1² + p.).
(28)
Expressing each in terms of d; = potts :
K
d; +
d. + t
0
(29)
d,
-
d, -
i=1
Ignoring first-order terms and constants and focusing on the dominant components, we obtain the final expression:
K
d; +
14 + 0z
E
+
E(x)
x
Ri
d,
(30)
d,
1
P4: 0,
C Detailed Proof of Scaling-Invariant Tone Mapping
We construct the tone mapper TMst by a neural network composed of layers and remove all bias terms within this network. Then start proving the TM is functionally
with
16

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Table Performance comparison with the radiance range of neural photometric on RoD Dataset. Bold denotes the default setting.
Radiance Range
[1c3, 1e6
[1c3,
le7]
[1c3, 1e8]
[1e4, 1e7]
[1e4, 1e8)
[1e5, 1e7]
[1e5, 1e8)
mAP
49.7
49.8
49.9
49.8
49.7
49.6
49.6
mAR
58.6
58.7
58.7
58.7
58.7
58.6
58.5
Table 8: Quantitative comparison of different pretraining losses.
Pretrained Loss
Method
mAP
AP50
AP75
contrast
L. NL.D
41.3
67.1
48.2
0.08
Faster R-CNN
4
S3
49.8
73.3
55.6
0.411
equivalent to local tone mapping operator. Convolution: For input x and kernel K;;
conv = a (2),,())) where conv,(x) = K₁ x.
(31)
Batch Normalization: For input x and kernel K₃:
x 14(2))
(N.(x) 7
(32)
(1(2))
where (((az)= a (z(z) and (,(ax) (z). ReLU: For input x:
RELU(x) = max(x:; 0).
(33)
Then we start proving this bias-free network is scaling-ing innaiin-nvaariant:::
TMsi(a x) = RELU BN, o K2 * .... RELU BN, K1 * (ay)
RELU BN, Kg RELU BN1 (a K y)
#Convolution Linearity
a K1 x a. 11
RELU BÑ, Kg
RELU 7/1
#BN statistic scaling
Q-01
K1 *
RELU BN, Kg .... RELU '/-0
01
K1 *
RELU BN, K2 .... REEU
?11
# Homogeneity
T1
RELU BN, K2, o...o RELU BN, o K1 #
a TMss (z).
(34)
where denotes the cascading of network layers. In the scaling-invariant transformation, all operators apply a linear transformation on local neighborhoods. Here, BN, adaptively adjusts gains using local statisties (μ4 σ²). and the cascade of BN, and ReLU activation mimics tone curves that compress highlights and shadows while preserving tones. Since all these linear transformations are applied within a local window, they effectively function as local tone mapping.
D More Ablation Studies
Ablation of Radiance Range. In the proposed neural photometric calibration, we set the radiance range as a hyperparameter. We conduct an ablation study on radiance and detection performance, with the results shown in Table The findings demonstrate that our neural photometric calibration is sensitive the radiance range, highlighting the method's robustness in handling varying lighting conditions.
Ablation of Pretraining Loss. We conduct an ablation study on pretraining loss to demonstrate its effect on convergence performance. We compare the pretraining loss between NLPD [3) and LI loss, with the results shown Table g The LI loss is supervised by HDR ISP results. The experiment shows that NLPD pretraining enables the tone mapper to enhance details, thereby improving detection performance.
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Ours
IDRR ISP
CLAHIE94
RAODNe123
ZeroDCEI 11
ReconfigISP21
Figure 8: Visual comparison of different methods HDR RAW inputs. The first shows day scenes, while the second row presents night scenes. Our method outperforms the comparison methods. Please zoom in for confidence scores and class predictions.
Ours
HDR ISP
CLAHE94
ZeroDCE--21
Reconlig1SP21
Figure 9: Visual comparison different methods on HDR RAW inputs. The first row shows day scenes, while the second row presents night scenes. Our method outperforms the comparison methods. Please zoom for confidence scores and class predictions.
E
More Visual Comparison.
We show more visual results in Fig. and Fig. Specifically, we visualize the detection results of the comparison methods with confidence scores greater than 0.3, in different scenarios of the RoD dataset [15] In the first row of Fig. 8 the tunnel environment is shown, where our method effectively reduces false detections. Fig. 9 shows typical HDR scene, where our method detects small objects that other methods fail to identify.
F
Real World Evaluation.
HDR Video Validation. We collect HDR RAW video sequences from autonomous driving scenes to validate the proposed method, using YOLOv3 [5] as the base detector for this validation. The entire pipeline is evaluated on the NVIDIA Jetson AGX Orin (16-bit float) with 2K resolution (2048 1080) input. The HDR RAW input is initially resized to 4K resolution (4096 2160) for processing. Additionally, we have created video demo (video_demo.mg demo.mpp) in the supplementary file showcase the detection results the video sequences. Please refer to the attachment. We will release the video sequences and corresponding annotations once the dataset complete.
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NeurIPS Paper Checklist
1. Claims
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We claim our main contribution in abstract, introduction | and conclusion
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Justification: We discuss the limitations of proposed work Ablation Studies
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Justification: We provide the full assumptions and proof of the proposed general distortion model and progressive thin-plate spline model, the citation and reference are provided Sec.
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Justification: We have fully disclosed the experimental details in Sec. Since we propose novel image rectified model, we clearly describe the specific module architecture and we demonstrate the processing of equipped training strategy for multi-task learning in Sec.
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We use opensource datasets (mentioned in Sec. 4 Implementation Details) and the experiment details are summarized at Sec. Implementation Details. Our code will be available after submission.
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Answer: [Yes]
Justification: We demonstrate the involved computer resources (Computatiinal performance, time of execution, type of devices, and code framework) in Sec. Implementation Details.
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Subjects
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Answer: [NA]
Justification:
This paper does not involve LLMs as important components.
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Reviews:
[{"review_id": "j2AJmNKHo9", "reviewer": "Reviewer_Vk4C", "summary": "The paper proposes a tone mapping method that can be jointly trained end-to-end with neural object detection models. The method first applies a neural photometric calibration module to handle varying lighting conditions, followed by a scale-invariant tone mapping module. The entire pipeline is optimized jointly with the object detector to improve detection performance under diverse illumination.", "questions": "- Could the authors clarify why joint training of the tone mapper and object detector is preferable compared to using a modular tone mapping approach followed by detection?\n- Are there scenarios where the added complexity of joint training is justified, and if so, could the authors provide practical examples or empirical evidence illustrating these benefits?\n\nI am not an expert in this specific area, so I am open to reconsidering my rating if there are domain-specific factors I may have overlooked.", "limitations": "-", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The proposed method improves object detection performance when jointly trained with several commonly used detectors.\n- The approach demonstrates low latency, making it practical for real-time deployment.\n\nWeaknesses:\n- Since the proposed tone mapping is specifically trained end-to-end with the detector, it is somewhat expected that detection performance would improve under this setup.\n- It is unclear why this design choice is preferable, as joint end-to-end training sacrifices modularity and increases training pipeline complexity. This seems like a significant disadvantage that may outweigh the potential benefits of joint optimization, and the paper currently does not provide a clear justification for this trade-off.", "was_revised": true, "final_justification": "I appreciate the additional experiments provided by the authors; however, they confirm my concerns about the lack of modularity. In my view, the need to jointly retrain the tonemapper for every new task introduces an additional level of complexity and is a clear disadvantage. Therefore, I will maintain my original rating.", "rebuttal": "Dear Reviewer Vk4C,\n\nThank you for your feedback. We’d like to provide some contextual information to help you better understand our method and address your concerns.\n\n## Learning-based HDR Perception\n(1) HDR data contains richer tones and more details, which have the potential to boost various computer vision tasks. However, neural networks are usually designed for LDR input, which requires compressing the dynamic range before directly utilizing HDR data.\n\n(2) There are many ways to compress the dynamic range, with tone mapping being the most common. Handcrafted tone mapping methods are based on human visual perception, while deep learning-based tone mapping is guided by the loss function.\n\n## Concerns\n\n## Q1. Why is joint training of the tone mapper and object detector preferable compared to using a modular tone mapping approach followed by detection?\n\nThe HDR images present an extreme dynamic range compared to LDR images. For example, 24-bit HDR contains about 1e7 times the dynamic range of 12-bit LDR, directly feeding these HDR images into neural network can lead to gradient explosion.\nTherefore, tone mapping is necessary to compress the dynamic range initially.\nMost modular tone mapping methods are crafted to adhere to human visual perception, but this alignment may not always be conducive to meeting the demands of downstream high-level tasks.\nSince a DNN-based tone mapper is cascaded with an object detector, which optimizes the model parameters driven by detection loss, it can generate the most suitable image for enhanced detection performance.\nBesides, some of these DNN-based tone mappers are plug-and-play (such as our method) and can be cascaded with any object detector without any internal changes, which can also be seen as a modular approach.\n\n\n\n## Q2. Are there scenarios where the added complexity of joint training is justified, and if so, could the authors provide practical examples or empirical evidence illustrating these benefits?\nIn challenging imaging conditions (e.g., high dynamic range scenarios, rainy/haze weather), target detection requires a specialized image preprocessor. \nSome handcrafted image preprocessing methods provide limited generality, as they cannot generalize well across all examples. \nTo address this, cascading a DNN-based image preprocessor (eg, tone mapper,  dehaze network) with an object detector can improve performance, although it introduces additional complexity."}, {"review_id": "KA1Uf1yyut", "reviewer": "Reviewer_4R6p", "summary": "This work presents a method for HDR tonemapping as an input to detection networks. The authors present their method as an ISP-like approach that processes HDR captures fed into a pre-trained detection network. The method is evaluated on existing HDR datasets and using a prototype system that runs at real-time rates. The evaluation is focused on assessing various ISP and pre-processing methods that confirm the approach achieves consistent improvement over existing methods.", "questions": "Please comment on the finetuning of the detection network and missing RAW evaluations as discussed above.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- The pre-processing module (the ISP) is evaluated and compared to existing baseline methods in detail.\n- The method is described in detail, and parameter settings are evaluated adequately.\n- The approach is implemented on a realt-time platform.\n\nWeaknesses:\n\n- It is unclear if the evaluation is fair. My main concerns is here that the authors have not commented on whether and how they fine-tune the downstream network. As they already use detection losses, it remains unclear if the network has been finetuned here. \n- As such, baseline evaluations without any ISP (finetuned on RAW data) are missing. \n- The tonemapper is a convolutional network with scale invariant BN, which offers little technical novelty.\n\nAs such, overall, without extensive evaluations on fine-tuning I remain on the fence in this stage of the review process.", "was_revised": true, "final_justification": "I have increased my rating as the rebuttal addressed my concerns around major gaps in the evaluation. That said, concerns around novelty and technical contribution still remain, as also acknowledged by the authors (core contribution being a scale-invariant tonemapper).", "rebuttal": "Dear Reviewer 4R6p,\n\nThank you very much for your insightful and valuable feedback. We believe there might be some misunderstandings, and we sincerely hope to clarify them and adequately address your concerns.\n\n## Misunderstandings\n\n## Q1.  Evaluations without ISP (HDR RAW Data).\n\nActually, we have reported the performances of using HDR RAW data directly and HDR ISP in **Table 1** and present the data point as follows.\n\n| Pipeline           | mAP   | AP50 | AP75 | AR75 |\n| ------------------ | ----- | ---- | ---- | ---- |\n| HDR RAW （Direct） | —NAN— |      |      |      |\n| HDR ISP            | 42.9  | 65.9 | 46.4 | 49.6 |\n| Our Method         | 49.8  | 73.3 | 55.6 | 58.7 |\n\nFor HDR RAW data, we cannot get normal results (0.049, 0.092, 0.032, 0.059) for training collapse.  It is important to note that 24-bit HDR RAW data has an extremely large dynamic range.  Directly using HDR RAW data can lead to gradient explosion, causing the loss function to become **NaN**, which makes the training process **non-convergent**.\n\nRegarding HDR ISP, the results of other methods are significantly lower than those of our approach.\n\nFurthermore, Reviewer 2qV1 acknowledged that our framework demonstrates improved performance compared to traditional ISP pipelines. Reviewer zmDQ regarded our method as having outstanding performance, notably outperforming existing state-of-the-art techniques.\n\n\n\n## Q2. Missing the details on whether and how they fine-tune the downstream network.\n\nIn this paper, we cascade the proposed tone mapping framework with the object detection network. The detection network is initialized with COCO pretrained weights and jointly trained with the tone mapping framework using detection loss in an end-to-end manner. Apart from this, we did not perform any fine-tuning on the detection network.\n\n## Concerns\n\n## Q3. It is unclear if the evaluation is fair. \n\nFollowing the RoD dataset [1], we choose the RAODNet23 [1], IANet22 [2], which is most relevant works to ours\n\nFor handcrafted tone mapping methods, we select Mantiuk08 [4] and CLAHE [5], which are included in popular development packages and widely used in applications.\n\nWe also compare other types of comparative methods, including the Differentiable ISP Method, Low-Light Enhancement Method, and DNN-based ISP Network.\n\nAmong these, RAODNet23 [1] serves as the strong baseline method for the RoD dataset, and our method shows significant improvement in comparison (+3.7mAP).\n\n\n## Q4. The tone mapper offers little technical novelty.\n\nThe innovativeness of our design is reflected in the following three aspects:\n\n1. The structure we proposed possesses **scale-invariant** properties, which are supported by **mathematical theories**. Tone Mapping is employed to compress the dynamic range and preserve details when converting from HDR to LDR. Theoretically, tone mapping necessitates the capacity to map HDR to LDR on a per - pixel basis under arbitrary luminance conditions. This implies that tone mapping must consider all scaling factors, with particular inclusion of the origin (mapping any value to zero). This realization has inspired us to design a scale-invariant tone mapper by eliminating all bias terms. We have proven that bias-free CNNs are scale-invariant, as shown in Eq. 8, and are also equivalent to local tone mapping, as demonstrated in Eq. 24 (in the Appendix).\n\n2. Compared with other methods, the design of the tone mapper is **efficient and concise**, eschewing complex predefined models. In contrast to related works such as [1], [2], and [3], these approaches pre - define tone curves and estimate curve parameters to dynamically compress the dynamic range for each input. Moreover, the most recent tone-mapping methods, like [4] and [5], utilize Look-Up Tables (LUTs) and pyramid decomposition. This results in a more complex pipeline that is not suitable for deployment on edge devices.\n\n3. Our method holds significant **practical** value. It exhibits high-speed performance and incurs a lower computational cost. All reviewers, including you, have acknowledged that our method is highly suitable for real-world applications.\n\n   \n\n\n\n[1] Xu, Ruikang, et al. \"Toward raw object detection: A new benchmark and a new model.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n[2] Liu, Wenyu, et al. \"Image-adaptive YOLO for object detection in adverse weather conditions.\" Proceedings of the AAAI conference on artificial intelligence. Vol. 36. No. 2. 2022.\n\n[3] Li, Chongyi, Chunle Guo, and Chen Change Loy. \"Learning to enhance low-light image via zero-reference deep curve estimation.\" IEEE transactions on pattern analysis and machine intelligence 44.8 (2021): 4225-4238.\n\n[4] Jiang, Ting, et al. \"Meflut: Unsupervised 1d lookup tables for multi-exposure image fusion.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.\n\n[5]  Zhang, Feng, et al. \"Lookup table meets local laplacian filter: pyramid reconstruction network for tone mapping.\" Advances in Neural Information Processing Systems 36 (2023): 57558-57569."}, {"review_id": "nPMz8w0Yc8", "reviewer": "Reviewer_2qV1", "summary": "This paper introduces a novel tone mapping framework that bridges the gap between HDR RAW inputs and LDR sRGB requirements for deeplearning alghrithoms, enabling end-to-end optimization with the object detection task.\nThe proposed approach replaces traditional ISP pipelines with neural photometric calibration and a scaling-invariant local tone mapping module to regulate dynamic ranges while preserving details.\nIt outperforms existing tone mapping and AI-ISP methods.", "questions": "See Weaknesses.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The proposed framework jointly optimizes tone mapping and downstream detection tasks with improved performance over traditional ISP pipelines and existing improvement strategies.\n2. Fine-tuning from LDR to HDR models can be supported at a much lower computational cost, increasing the practicality for real-world applications. \n3. The efficiency of the proposed framework ensures high speed performance on embedded hardware suitable for autonomous driving applications.\n\nWeaknesses:\n1. Why is the learnable transformation function in Equation 4 designed in this particular form?\n2. There is some ambiguity in the model description regarding the use of \"Tone Mapper.\" From the contributions, Figure 2, and Section 3.3, it appears that the Tone Mapper is only one component of the proposed tone mapping framework, working alongside neural photometric calibration. However, in Sections 3.4 and 4.3 discussing model optimization, \"Tone Mapper\" seems to refer collectively to both components.\n3. In the experiments of Table 5, how exactly was the scaling-invariant property removed?\n4. In Table 1, different algorithms should be clearly labeled with their respective grouping characteristics to better highlight the focus of each algorithm category.", "was_revised": true, "final_justification": "The author's response addressed my concerns, and I will maintain my original rating.", "rebuttal": "Dear Reviewer 2qV1,\n\nWe'd like to thank you for your positive feedback and address the concerns raised in your comments\n\n\n\n## Concerns\n\n## Q1. Why is the learnable transformation function in Equation 4 designed in this particular form?\n\nSince the input HDR RAW images are recorded as digital numbers, we need to make educated guesses about the minimum and maximum radiance values in the original scene. We introduce Eq. 4, where the bias term is constant to avoid excessively small values, and the scale map consists of learnable parameters that adjust the dynamic range. This approach maps HDR inputs into a unified luminance space through neural photometric calibration, addressing the large diversity in lighting conditions (e.g., day/night, mixed scenes).\n\n\n\n## Q2. There is some ambiguity in the model description regarding the use of \"Tone Mapper.\"\n\nThank you for your suggestion. We will further clarify the description and distinguish the details between the tone mapper and neural photometric calibration in the paper. The tone mapping framework includes the scale-invariant tone mapper and neural photometric calibration, which work together for object detection. In Section 3.4, we pretrain only the scale-invariant tone mapper to warm up the parameters, enabling the tone mapper to gain the ability to compress the dynamic range. In Section 4.3, under the performance transfer strategy, we fine-tune the parameters of the tone mapping framework (tone mapper + neural photometric calibration) and the detection head to achieve rapid adaptation from the LDR RGB domain to the HDR RAW domain.\n\n\n\n## Q3. In the experiments of Table 5, how exactly was the scaling-invariant property removed?\n\nScale invariance means that the output changes in the same scale as the input. For HDR-to-LDR tone mapping, we argue that if the tone mapping network operates by scaling the HDR image onto a linear space of LDR images, then that space should encompass all rescalings of those images, especially including the origin. To achieve this, we design a bias-free CNN where, for any input $x$, the components (convolution, ReLU, and batch normalization) satisfy $f(ax)=af(x)$ for $x>0$. This ensures that any negative entries in the input are set to zero. Multiplying by a nonnegative constant does not change the sign of the entries in a vector. \nThus, the bias-free network remains scale-invariant and can rescale the image without introducing any bias value. In contrast, a bias network would introduce additional background noise in the resulting images.\n\n\n\n## Q4. In Table 1, different algorithms should be clearly labeled with their respective grouping characteristics to better highlight the focus of each algorithm category.\n\n\n\nThank you for your suggestion. We will add the category group and modify Table 1 to the following style.\n\n| Method Group                | Methods       |\n| --------------------------- | ------------- |\n| Direct                      | HDR RAW       |\n| ISP Pipeline                | HDR ISP       |\n| Tone Mapping                | Mantiuk08     |\n|                             | CLAHE94       |\n| Differentiable ISP Pipeline | AnscombeNet21 |\n|                             | ReconfigISP21 |\n| Low-Light Enhance           | SCI22         |\n|                             | Zero-DCE++21  |\n| Differentiable Tone Mapping | IANet22       |\n|                             | RAODNet23     |\n|                             | RawOrCooked23 |\n|                             | Ours          |"}, {"review_id": "jCDMpxLaBR", "reviewer": "Reviewer_zmDQ", "summary": "The paper proposes a real-time scene-adaptive tone mapping method. Its core consists of a neural photometric calibration module for dynamically regularizing extreme dynamic ranges, and a scale-invariant local tone mapper that ensures linear response of mapping functions to input luminance scales, enhancing detail preservation and detection performance. Through end-to-end joint training optimization, this method improves the accuracy and generalization capability of HDR object detection.", "questions": "- If the dynamic range of HDR data is proportional to the square of the gradient magnitude, then why does training a detector based on HDR inputs lead to unstable gradient propagation, while training an HDR-to-LDR network does not exhibit such issues? Has there been any experimental analysis on the training stability with HDR inputs?\n\n- Neural Photometric Calibration is performed in a low-resolution image space, which means that the predicted weight map is patch-wise. Approximately how large is each patch? Would this patch-wise weight map affect the scale-invariance of subsequent processing?\n\n- The scale-invariant local tone mapping network proposed in the paper can effectively preserve the texture details of the image, while HDRNet is also a commonly used local tone mapping technique. The authors may consider comparing the differences between these two methods.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- The paper  is clearly written and easy to comprehend.\n\n- The paper proposes an innovative neural photometric calibration module along with a scale-invariant tone mapping network, which effectively enhances detection performance.\n\n- In detection experiments under HDR scenarios, it also demonstrates outstanding performance, significantly surpassing existing state-of-the-art techniques.", "was_revised": true, "final_justification": "The author's rebuttal has addressed most of my concerns.", "rebuttal": "Dear Reviewer zmDQ,\n\nWe'd like to thank you for your positive feedback and address the concerns raised in your comments.\n\n\n\n## Concerns\n\n## Q1. Why does training a detector based on HDR inputs lead to unstable gradient propagation, while training an HDR-to-LDR network does not exhibit such issues? \n\n1.The 24-bit HDR (140dB) images exhibit an extreme dynamic range compared to 12-bit LDR images (70dB), with a ratio of approximately 10^7.\nTherefore, directly feeding HDR images into the network can result in extreme gradient fluctuations, potentially causing the loss function to become **NaN**.\n\n2.Both HDR-to-LDR models and handcrafted tone mapping algorithms can compress the dynamic range while retaining as many details as possible, helping to stabilize the training process. The difference lies in the fact that the effectiveness of HDR-to-LDR models is influenced by network design and the training process. This is why we pretrain the tone mapper network initially. Traditional tone mapping algorithms, on the other hand, do not require pretraining of parameters, allowing them to converge stably.\n\n\n\n## Q2.Has there been any experimental analysis on the training stability with HDR inputs?\n\nWe use a reinahrd tone mapping [1] method  to compress the dynamic range and quantize it into different bit-depths before feeding it into the detector.\nExperiments show that 14-bit data yields the best performance, while 8-bit data yields the worst.\nA wider dynamic range preserves more useful details, but under extreme lighting conditions, it can render most details indistinguishable, making it challenging for neural networks to extract meaningful feature.\n\n| Input  | mAP   | AP50 | AP75 |\n| ------ | ----- | ---- | ---- |\n| 24-bit | —NAN— |      |      |\n| 14-bit | 48.1  | 69.4 | 51.4 |\n| 12-bit | 47.8  | 69.0 | 47.8 |\n| 8-bit  | 43.8  | 65.6 | 45.8 |\n\n\n\n\n\n## Q3. What is the size the predicted weight map in low-resolution ? Would this patch-wise weight map affect the scale-invariance of subsequent processing?\n\nOur method predicts the scale map $K$ in a low-resolution space (256x256) and then upsamples it to the original resolution using bilinear interpolation.\nThis scale map  does not affect the scale-invariant property of the tone mapper, which is determined by the network design.\nBias-free CNNs (with BN and ReLU) generally exhibit scale invariance.\n\n\n\n\n\n## Q4. The authors may consider comparing with HDRNet.\n\nWe compared HDRNet[2] and the results are as follows.\n\n| Pipeline                       | mAP   | AP50 | AP75 | AR75 |\n| ------------------------------ | ----- | ---- | ---- | ---- |\n| HDRNet (from scratch)          | —NAN— |      |      |      |\n| HDRNet (pretrained from MIT5K) | 41.9  | 64.9 | 46.1 | 49.2 |\n| Our Method                     | 49.8  | 73.3 | 55.6 | 58.7 |\n\n\nHDRNet (from scratch) refers to a cascade with a detector trained from scratch, while HDRNet (pretrained) is pretrained on the RoD dataset (HDR-LDR pairs).\n\n\n\n\n\n\n[1] Reinhard, Erik, et al. \"Photographic tone reproduction for digital images.\" Seminal Graphics Papers: Pushing the Boundaries, Volume 2. 2023. 661-670.\n\n[2] Gharbi, Michaël, et al. \"Deep bilateral learning for real-time image enhancement.\" ACM Transactions on Graphics (TOG) 36.4 (2017): 1-12."}]

Author rebuttal:

Meta-review: This paper proposes a real-time scene-adaptive tone mapping method. The core novelty is a scale-invariant local tone mapper that ensures linear response of mapping functions to input luminance scales, enhancing detail preservation and detection performance. This method improves the accuracy and generalization capability of HDR object detection through end-to-end joint training optimization.

Reviewers acknowledge that the proposed method improves the HDR object detection and real-time performance. Meanwhile, they raise several concerns about the lack of novelty and practicality (optimization for each task).

Overall, the major weaknesses raised are:
1) Novelty and technical contribution, which falls in a scale-invariant tonemapper using scale-invariant BN.
2) There is a need for joint training of the tonemapper and each downstream task.

Despite those weaknesses, we would count the positive side of the paper; the conceptual novelty of joint training of HDR tone-mapping and downstream task (i.e., object detection) as potentially helpful for driving scenarios.
Decision: Accept (poster)