UltraLED: Learning to See Everything in Ultra-High Dynamic Range Scenes
Authors: Yuang Meng
Xin Jin
Lina Lei
Chun-Le Guo
Chongyi Li
Venue: NeurIPS 2025 poster ()
Keywords: High dynamic range
Low-light image enhancement
Raw denoise
Primary area: applications

Abstract: Ultra-high dynamic range (UHDR) scenes exhibit pronounced exposure disparities between bright and dark regions. Such conditions are Ultra-high dynamic range (UHDR) scenes exhibit significant exposure disparities between bright and dark regions. Such conditions are commonly encountered in nighttime scenes with light sources. Even with standard exposure settings, a bimodal intensity distribution with boundary peaks often emerges, making it difficult to preserve both highlight and shadow details simultaneously. RGB-based bracketing methods can capture details at both ends using short-long exposure pairs, but are susceptible to misalignment and ghosting artifacts. We found that a short-exposure image already retains sufficient highlight detail. The main challenge of UHDR reconstruction lies in denoising and recovering information in dark regions. In comparison to the RGB images, RAW images, thanks to their higher bit depth and more predictable noise characteristics, offer greater potential for addressing this challenge. This raises a key question: can we learn to see everything in UHDR scenes using only a single short-exposure RAW image? In this study, we rely solely on a single short-exposure frame, which inherently avoids ghosting and motion blur, making it particularly robust in dynamic scenes. To achieve that, we introduce UltraLED, a two-stage framework that performs exposure correction via a ratio map to balance dynamic range, followed by a brightness-aware RAW denoiser to enhance detail recovery in dark regions. To support this setting, we design a 9-stop bracketing pipeline to synthesize realistic UHDR images and contribute a corresponding dataset based on diverse scenes, using only the shortest exposure as input for reconstruction. Extensive experiments show that UltraLED significantly outperforms existing single-frame approaches. Our code and dataset are made publicly available at https://srameo.github.io/projects/ultraled.

UltraLED: Learning to See Everything in Ultra-High Dynamic Range Scenes
Yuang Meng"
Xin Jin" Lina Lei Chun-Le Guo
Chongyi Li
VCIP, CS, Nankai University
https://sranoo github so/projjctts/lbtroll
Abstract
Ultra-high dynamic range (UHDR) scenes exhibit significant exposure disparities between bright and dark regions. Such conditions are commonly encountered in nighttime scenes with light sources. Even with standard exposure settings, bimodal intensity distribution with boundary peaks often emerges, making it difficult to preserve both highlight and shadow details simultaneously. RGB-based bracketing methods but are susceptible to misalignment and ghosting artifacts. We found that a short- exposure image already retains sufficient highlight detail. The main challenge of UHDR reconstruction lies in denoising and recovering information in dark regions. In comparison the RGB images, RAW images, thanks their higher bit depth and more predictable noise characteristics, offer greater potential for addressing this challenge. This raises key question: can learn to see everything in UHDR scenes using only single short-exposure RAW image? In this study, we rely solely a single short-exposure frame, which inherently avoids ghosting and motion blur, making it particularly robust in dynamic scenes. To achieve that, we introduce UltraLED, two-stage framework that performs exposure correction via ratio map to balance dynamic range, followed by brightness-aware RAW denoiser enhance detail recovery in dark regions. To support this setting, we design a 9-stop bracketing pipeline to synthesize realistic UHDR images and contribute a corresponding dataset based on diverse scenes, using only the shortest exposure as input for reconstruction. Extensive experiments show that UltraLED significantly outperforms existing single-frame approaches. Our code and dataset are made publicly available at httpp://saneeggguubbo/oogoggessp githubbio/rrojectsslllaaadd
capture details both ends using short-long exposure pairs,
Introduction
UHDR scenes are common in daily life, such night streets illuminated by headlights and streetlights, or indoor environments with bright window light and dim interiors. The extreme contrast these scenes exceeds the capacity of conventional cameras in single exposure, often resulting in bimodal intensity distributions, where highlight and shadow regions form separate peaks with little midtone. This phenomenon makes it difficult for cameras retain details across the full brightness range, often causing loss of visual information and reduced image fidelity.
7 36 to capture sequence of exposures ranging from short to long. Nevertheless, this kind of approach inherently suffers from inter-frame motion, resulting in misalignment and ghosting artifacts that degrade reconstruction quality. Some generative approaches 330 hallucinate missing highlights from single long-exposure input (i.e., Fig. [a)) yet
Recent methods often use bracketing 17 56
"Equal Contribution.
fC. L. Guo is the corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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RGB Input
RAW Input
(a) Long- exposure Input
(b) Flux Fill 30
with long-exposure
(c) ELD
48
with sbont-exossuee
(d) Short-exposure Inpot
(c) RetinexMamba
2 with short-exposure
(f) UltraLED (Ours) with short-cxposure
Figure 1: Visualization results of different methods for UHDR scene reconstruction.
struggle to recover realistic details saturated regions as shown in Fig. [b). In addition, some methods 2 adopt short-exposure imputs (i.e., Fig. (T)) to avoid highlight clipping. Nevertheless, due to the limited dynamic range of 8-bit RGB images, shadow regions may be represented with as few 4 bits, causing severe quantization artifacts and noise, shown in Fig. ee).
The observations in Fig. D lead to two critical conclusions: 1) long- nung-exposure-busee methods fun- damentally fail to recover highlight regions in the absence of short-exposure references; and 2) short-exposure-bate based methods are primarily limited by bit depth constraints and noise in dark re- gions. Fortunately, RAW images can offer substantial advantages for such tasks due to their higher bit depth and simpler noise distributions. As shown in the red box of Fig. [ the globe on the right, which is in darkness, has been restored result similar to that of a long exposure. The results show that RAW images can preserve more complete and realistic details even under extremely low-light conditions. Therefore, for short-exposure RAW images, only need to perform exposure correction, as they already contain both the unclipped highlights and the recovered details from low-light regions. Nevertheless, two core challenges still remain:
Extreme exposure differences compounded with severe noise in low-light regions result in a highly ill ill-posed reconstruction problem. Jointly optimizing exposure correction and denoising degrades both learning efficiency and reconstruction accuracy.
In the RAW domain, complex noise models extremely low-light conditions, whereas simpler models such as the PG noise model perform better in brighter regions
48
demonstrate superior effectiveness under
(28). This discrepancy stems from the fact that the noise distribution tends to follow different known distributions as the image brightness changes. Therefore, it is challenging to simultaneously reconstruct the bright and dark areas of UHDR scenes.
To address these challenges, we decouple the UHDR reconstruction process into two distinct stages. First, leveraging the high bit-depth property of RAW images, we train network to generate ratio map that dynamically corrects local exposure across the image. Second, recognizing that noise characteristics vary with brightness, we introduce a brightness-aware noise model. To guide the denoising network, we encode brightness information using a ratio map, enabling joint reconstruction of both bright and dark regions.
In addition, there no publicly available dataset suitable for training and evaluating UHDR recon- struction methods. To bridge this gap, we develop novel pipeline that leverages the characteristics of RAW images to synthesize 9-stop exposure stack, from which we separately obtain noise-free ratio maps and the corresponding fused results. Based on this pipeline, we construct a high-qualit,

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dataset tailored for UHDR reconstruction. Notably, all scenes are captured under static conditions, enabling exlti-exposuue acquisition without alignment issues or ghosting artifacts.
Our main contributions are summarized as follows:
We propose a novel method for reconstructing UHDR scenes using only a single-frame RAW image, in which two-stage pipeline is proposed to decouple exposure correction from denoising, fully leveraging the properties and advantages of RAW data.
We propose brightness- aware noise model and a ratio map encoding scheme that synergis- tically guide the network in recovering fine details across varying exposure levels.
We design a new data pipeline for multi-exposure fusion and contribute a corresponding dataset for benchmarking UHDR reconstruction performance.
2
Related Work
RAW Denoising. Since the pioneering work of SIDD [1] the potential of RAW data for low-light imaging has been extensively explored. Recent studies simulate low-light conditions by degrading normal-light images. SID [10] introduces real-world RAW dataset to train denoising networks. Subsequent approaches have sought to model noise more precisely via either physically calibrated
28 achieving improved denoising parameter estimation 48 (54] or network-based learning 9 40
performance. However, existing RAW denoising methods 10]
28 48 26] cannot adaptively regulate local exposure, rendering them inadequate for UHDR scenarios. In contrast, UltraLED enables pixel- wise brightness adjustment, simultancously achieving effective denoising and exposure correction.
Single-image HDR and Low-light Image Enhancement. Both single-image HDR reconstruc- tion and low-light image enhancement seek to produce well-exposed images with rich detail [33]. Traditional single-image HDR techniques 55 3 4 rely on internal cues to predict scene luminance and extend dynamic range by estimating light source intensity. However, both approaches are funda- mentally constrained by the absence of scene information, which is inherently difficult recover 20 35). Single-image HDR methods typically operate on normally exposed inputs [20 37 35), requiring generative capabilities to reconstruct the lost details caused by overexposure [56 52 24
This task becomes particularly challenging in cases of severe highlight clipping. Traditional retinex-
based low-light enhancement methods 45 31 46 22 & decompose an image into reflectance and illumination components, treating the task as illumination estimation. Most low-light enhancement methods 23 25 32 34 (77 operate in the RGB domain, where the limited bit depth, sometimes reduced to only 4 bits in dark regions, significantly limits the fidelity of reconstruction. We address this issue by operating in the RAW domain, where images retain higher bit depth (typically 14 bits). Although certain regions are quantized to 4 bits in RGB, they may preserve up to 10 bits in RAW, allowing for more accurate and faithful recovery. Meanwhile, the high-bit information in the RAW domain preserves more predictable and simpler distribution of the original noise [10 48), making it easier to denoise. By denoising directly in the RAW domain, UltraLED surpasses RGB-based achieving superior detail recovery under short exposures.
approaches [0 48
Multi-exposure Fusion HDR. Traditional approaches typically reconstruct HDR images by merging a sequence of bracketed low dynamic range (LDR) images [38] [7], where the alignment of multiple
exposures remains the most challenging step. Several CNN-based methods (2 50 49 have achieved improved alignment performance. Peng et al. [41] and FlowNet 19 explore optical flow estimation techniques for more accurate alignment. Given the limited receptive field of CNNs, transformer- based approaches [511 (3) have demonstrated superior performance. More recently, cnffuusion-based methods [56 14] have also shown promising results; however, they require substantial computational resources and often produce unrealistic details due to the generative nature of the models. Multi- exposure fusion HDR captures true scene content across exposure extremes but often suffers from alignment errors and ghosting, especially in dynamic or low-light scenes. Single-image methods using long exposure struggle to recover highlight details, while those based on short-exposure RGB inputs face severe quantization and noise in dark regions due to limited bit depth. We address these issues by reconstructing UHDR scenes from a single -eoot-exposure RAW image, achieving better fidelity with lower cost and complexity.
3

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N2
Brightness
Noise
Amplification
Eddition
Enightene Region
EV.0
F7-2
FI-N
Enc.
Exposure Fusion
Pseudo Multi-Exposure Images
Ratio Map Estimator
Raw Denoiser
Lyyllghted
CF
Training Data Synthesis Pipeline (Sec. B.1
UHDR Reconstruction Pipeline (Sec.
Figure 2: Overview of our framework, which is divided into two parts: 1) Training Data Synthesis Pipeline: clean and normally exposed image is used as input. The lighted image 4 is generated by artificially amplifying the brightness in specific regions of I. The noise model then applied to Iz to synthesize the corresponding noisy and overexposed image IN Meanwhile, I, linearly scaled down by different factors and clipped to produce pseudo multi-exposure images from EV-0 to EV-N. These images are fused using exposure fusion [8 to obtain LFF and a clean exposure-corrected map (ratio map 5) is derived by dividing T, by L.F; and 2) UHDR Reconstruction Pipeline: this pipeline consists of two stages, implemented using two UNet 10 First, the Ratio Map Estimator takes the
noisy and overexposed RAW image INL as input and outputs the ratio map S. Then, is used to cor- rect the exposure of IN producing noisy but well-exposed image IN. which is then passed into the RAW Denoiser for denoising guidance. Finally, the RAW Denoiser outputs clean and well-exposed RAW image ICF. Note that, in terms of inference, only UHDR Reconstruction Pipeline used.
Methodology
In this section, we introduce our training data synthesis pipeline and UHDR reconstruction pipeline. An overview of our framework is shown in Fig,
3.1 Training Data Synthesis Pipeline
Capturing perfectly aligned, noise-free multi multi-exposure images under unconstrained, real-world conditions remains highly challenging. Thus. it is non-trivial and often impractical to construct large-scale paired datasets directly from in-the-wild scenes. In this study, we leverage a large-scale repository of well-exposed RAW images to overcome this limitation and synthesize paired data for the whole processing pipeline. RAW images without highlight clipping exhibit línear relationship between light intensity and signal values, along with predictable noise distributions. This allows us to directly obtain underexposed inputs and multi-cxposure sequences, enabling the generation of well-exposed references. Since these well-exposed RAW images free from highlight clipping, we also simulate artificial overexposure for completeness. Accordingly, our training data synthesis pipeline comprises: 1) brightness amplification, 2) exposure fusion, and 3) noise modeling.
artificially synthesize the overexposure, following the procedure of previous work [16] 42 43 to generate saturated highlight regions. To further simulate the stochastic distribution of highlights in UHDR scenes, we inject additional random highlight patches. Brightened regions are randomly selected, intensified by stochastic gain factor, smoothed with a bilateral filter, and finally feathered at the boundaries with Gaussian kernel to simulate light diffusion. Given images that preserve highlight details, we construct pseudo multi-exposure images by progressively reducing global illumination, implemented as linear scaling for RAW data, followed by value clipping. Specifically, for the normalized image, the
Brightness Amplitication. To amplify the local image brightness,

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following formula to synthesize EV-i:
L 0, 1),
EV-i = clip 2
where i denotes the downscaling factor of the image's 's exposure value, I, denotes the artificially over-exposed RAW input, clip(r; 0, 1) represents clipping the value of x to the range between 0 and simulating overexposure truncation. The resulting exposures, spanning EV-0 to EV-N (where EV denotes exposure value, with each step doubling or halving incident light), are subsequently used in the HDR fusion stage.
Exposure Fusion. Because the pseudo multi-exposure images are synthesized by modulating the brightness of a single higheight-preserved RAW frame, inter-frame misalignment is inherently eliminated. We employ Exposure Fusion [38] to combine the images into a well-exposed composite. Since Exposure Fusion operates in the RGB domain, each RAW frame is first rendered to the RGB domain using predefined ISP parameters, after which the fused result is converted back to the RAW domain via the unprocessing pipeline [6]. This procedure yields noise-free, well-exposed reference image LL that has been lighted and fused, as well as a clean ratio map S, defined as
L S
ILF
Noise Model. We divide the noise in UHDR scenes into three parts, including hignal-dependent noise, signal-independent noise [48] 54 21). and brightness-aware noise. Our modeling of signal- dependent noise and signal-independent noise the as the existing method [48] in the RAW domain. Brightness-aware noise is introduced due to the significant brightness variations across different regions of UHDR scenes and the subsequent exposure correction process. Our final noisy input IN. representing the UHDR RAW image after exposure correction, is defined as:
Ir = SA(T), INZ = 11 + Nae + Nin, IN INL + Nba.
where 1, INL. Nae. and Nin denote the well-cxposed and noise-free RAW image, the UHDR image, the signal-dependem noise, and the signal-independent noise, respectively. No refers to the brightness-aware noise. SA denotes the processes of synthesizing artificial overexposure regions. The detailed formulation for synthesizing the final noisy image is given by:
IN SA (
(((( SA(T)) R--K (
K + Nin
R
where SA -1 denotes the processes of correcting the exposure using the ratio map estimator network, thereby modeling the No. component of noise. K is the random system gain for signal-dependent noise (shot noise), and is a random attenuation ratio for brightness.
For most RAW image denoising methods, in order to ensure numerical stability, the noisy image should be scaled to the original range. Up this point, have obtained all the reference images used for training, including the UHDR RAW image after exposure correction In as input, the ratio map S for exposure correction, and the noise-free and well-exposed ground truth image ILF.
3.2 UHDR Reconstruction Pipeline
Decoupling Exposure Correction and Denoising. We decouple the reconstruction of UHDR scenes into two parts: learning the exposure correction pattern of the image and the simple noise distribution, respectively. Specifically, the pipeline consists of three steps: 1) Using simple UNet that has the same architecture as that used in SID [00] to predict the ratio map S: 2) The predicted ratio map 5 is then applied to our UHDR input T. to construct well-exposed noisy input TN; 3) Finally, another same UNet is utilized to denoise N--
Ratio Map Encoding. The ratio map serves as a noise-free representation of image brightness. Our objective is to introduce an implicit feature capturing the relationship between brightness and noise.
5

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Regions with similar brightness levels tend to have similar ideal ratio values and correspondingly similar noise intensity levels. Since low-dimensional ratio values are insufficient represent the complex noise distribution across varying brightness conditions, we expand the dimensionality of the ratio map using Gaussian encoding. This is because Gaussian distributions can model the gradual change in noise intensity ratio values become closer. Additionally, we introduce weight corresponding to the ratio before encoding, which modulates the effective signal strength. For each ratio r, the encoding EC, is as follows:
(r-y) 2n
exp
EC,
(5)
2
where is an externally defined hyperparameter, and y vector representation obtained by uniformly sampling the ratio parameter space at a predefined dimensionality. The ratio parameter space should encompass all possible values that the ratio can take. The corresponding each pixel of the ratio map S; calculated as follows:
R 3
(6)
The integrated encoding scheme is injected into the denoising network through multi-layer per- ceptron and pero-initialized convolutional residual block, which enables patio-adaptive denoising guidance to better restore the details and textures under different ratios. This formulation ensures the denoiser dynamically adjusts its operation based amplification factors while maintaining noise suppression consistency across varying ratio conditions. More derivation and experiments can be found in the supplementary material.
Loss Functions. For our two-stage network, we employ different loss functions for different stages. In the exposure correction stage, due to the large range of values of our ratio map, we use a weighted LI loss [3] to ensure numerical stability instead standard LI loss:
N
S
Lweighted
S +
where is the numerical stability coefficient. The loss function for the denoising stage a standard LL -LL---ca Icrl
L1 loss: L1 N EN
Experiments
4.1 Proposed UHDR Dataset
To systematically assess the reconstruction performance of various methods on UHDR scenes, we construct UHDR image dataset comprising RAW images and their corresponding RGB counterparts. The thumbnail of our UHDR dataset is shown as Fig.
Figure 3: thumbnail of our UHDR dataset (convert to RGB images for visualization), where the images in the top row are UHDR inputs and the images the bottom row are ground truth. Our UHDR dataset comprises 24 UHDR scenes, the majority of which captured under three lighting conditions (ratio x50, x 100, and x200). For each combination of lighting condition and ISO setting, we captured three RAW images of the same scene ensure statistical reliability. In total, the dataset includes 585 paired RAW images for evaluation.

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To eliminate interference from ambient illumination, paired data are captured in a controlled indoor environment using a remotely operated Sony A7M4 camera. The experimental setup includes multiple types of light sources and diverse object arrangements, allowing directly illuminated objects to coexist with those shadow, thereby simulating real-world UHDR scenes. All surfaces within the shooting area, including tables and walls, are covered with anti-reflective fabric to minimize secondary reflections, ensuring that scene luminance is governed almost exclusively by direct illumination. For each scene, several sets of noisy images are recorded at different ISO settings according to ratio gradients of 50, 100, and 200. Unlike conventional low-light denoising datasets 48), here the "ratio" denotes the umplification factor required to achieve ideal exposure in the darkest region. High-quality reference images are obtained by capturing nine exposure values (EVs) s) at the lowest ISO with the corresponding longest exposure time, and synthesizing noise-free free and well-exposed ground truth via Exposure Fusion [38].
Notably, in contrast to typical HDR scenes, the illuminance of dark regions in UHDR dataset can be as low as 0.001-0.2 lux, whereas bright regions reach 0.3-100 lux. Consequently, in some scenes, maintaining proper exposure for the highlights forces the RGB pixel values (8 bit) in shadow areas to collapse to the range 0-4. Under such extreme conditions, RGB images are incapable of recovering shadow details, and UltraLED therefore outperforms RGB-based methods by substantial margin.
4.2 Implementation Details
Training Data and Network Architecture. We train our network using the synthesized data based on the ground truth of the RawHDR dataset (57), selected for its high bit depth, good exposure, and noise-free characteristics. Our ratio map estimator employs a simple U-Net architecture [44 to capture the characteristies of the brightness distribution. This module takes a noisy RAW image as input and outputs a noise-free ratio map. The hyperparameter a used to construct the ratio map encoding set to 30 in our experiments. For the denoising network, various architectures can be adopted. However, since the strength of our approach lies primarily in the overall pipeline and data processing strategy, we use the same simple U-Net framework [44] for a fair comparison with other methods. Note that employing more complex networks with higher computational capacity and parameters could further enhance performance. The related experiments are discussed in the supplementary material.
Optimization Strategy and Evaluation Metries. We first train the ratio map estimator network and then freeze its parameters before training the denoising network. For the denoising network, we adopt widely used training strategy for RAW domain denoising, as described in ELD 48 28]. The ratio map estimator requires relatively fewer training iterations, only 30,000 iterations using the Adam optimizer, with learning rate of 5 10-5, To comprehensively evaluate quantitative performance, we employ PSNR, SSIM, and LPIPS as evaluation metrics.
4.3 Comparison Experiments
We conduct both quantitative and qualitative evaluations on the UHDR dataset. Additionally, we observed that some low-light datasets, such as the SID dataset [10), often unintentionally capture UHDR scenes during outdoor night photography. Previous methods 488 have struggled accurately restore such scenes. To assess the generalization capability of our approach, we also perform qualitative evaluations on such scenes from the SID dataset.
To facilitate a comparison that better reflects actual visual perception, we convert all outputs to the RGB domain following the same ISP process. To ensure fair evaluations, we align the exposure levels of different methods' results using the approach proposed by RAWNeRF (39), thereby eliminating brightness variation as a confounding factor in image quality assessment. We then conduct compre- hensive comparisons between UltraLED and different approaches including low-light enhancement, annle-image HDR, as well as RAW domain denoising and fusion methods. is worth noting that we standardize the exposure of all RAW inputs using the commonly adopted method for RAW domain denoising 48), which calculates exposure ratios based on the ISO and shutter speed of the images. The inputs of different methods are as follow. 1) Short-exposure RAW input: Ultral_DD LED, RAW nomain-basee denoising followed by fusion methods including PG, ELD 48), and LED (8]. 2) Short-exposune RGB input: Low-light image enhancement methods including RetinexMamba 22
7

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EnlightenGAN [25], ZeroDDE 223. and Kind [55] 3) Long-exposure RGB input: Single-image
HDR methods including HDRUNet [2. HDRTVNet [3] and HDRTVNet++
Quantitative Results. As shown in Tab. I Tab. and Tab. 3 UltraLED demonstrates superior performance in handling UHDR scenes. In Tab. single-image HDR methods [12 R use long-exposure RGB images as input. The extended exposure time leads to severe overexposure and clipping, and due the lack of prior information, these HDR methods are unable to recover realistic details in heavily overexposed regions. This results in significant structural discrepancies between the outputs and the ground truth, reflected in low SSIM scores. However, since the inputs are noise-free, the PSNR remains relatively high. In Tab, 2 RAW domain denoising methods [48] (28) first process inputs with varying amplification ratios and then fuse the outputs using the same fusion method 38 adopted in our data synthesis pipeline. While this strategy yields high SSIM because of the structural similarity with the ground truth, it fails to account for the noise variation across different exposures, resulting in noise mixing and performance degradation. Additionally, the need for multiple denoising and fusion steps significantly increases their computational costs compared to ours. In operating in the RGB domain, Ultra..D leverages the higher bit depth RAW images, providing more comprehensive information. Furthermore, our decoupling strategy and brightness-awan noise modeling enable more effective denoising and detail restoration. We also conducted a user study to compare UltraLED with across different scenes and sensors. The results of this study are provided in the supplementary materials.
Tab. 3 compared to low-light image enhancement methods 225 23 55 2
other methods 48
2 2
Table 2: Comparison with RAW domain methods. LED [28] PG, and ELD 48 am- plify the input image by different ratios for denoising, and then fuse the results using the
Table 1: Comparison with HDR methods. HDR methods 112 3 ||I take long-exposure im- age as input. Although such input is noiseless, it suffers from highlight clipping in bright regions. The best result is marked in red.
same method 38 as producing ground truth.
The best result marked in red.
Methods HDRTTVNe VNEtT HDDRUNet UlraLEDD (Ours)
Methods
LED
PG
ELD
UlraLED (Ours)
PSNR 25.318 0,830
25.344 0.828
25,341
27.591
PSNR
25.879
25.142
25.942
27.591
SSIM
0.801
0.898
SSIM
0.888
0.878
0.886
0.898
LPIPS
0,095
0.095
0.104
0.075
LPIPS
0.083
0.101
0.081
0.075
Table 3: Comparison with low-light image enhancement methods. The best result is marked in red.
x50
x 100
x x200 SSIM
Methods
PSNR
SSIM
LPIPS
PSNR
SSIM
LPIPS
PSNR
LPIPS
EnlightenGAN
21.155
0.558
0.548
19.833
0.512
0.605
18.878
0,464
0.653
Zero-DCE
21.920
0.534
0.617
20.443
0.463
0,673
19.277
0.400
0.757
Kind
20.191
0.458
0.748
18.907
0.356
0.832
17.538
0.282
0.888
RetinexMamba
22.770
0.705
0.193
21.587
0.641
0.229
20.600
0.586
0.271
UltraLED (Ours)
27.591
0.898
0.075
27.327
0.887
0.095
27.091
0.860
0.121
Qualitative Comparison. As shown in Fig. 4 UltraLED outperforms existing approaches in UHDR scenes by achieving superior denoising and detail restoration in low-light areas, along with better color fidelity in bright regions. Fig. [ further demonstrates that UltraLED can simultaneously correct exposure and denoise, effectively recovering textures even severely overexposed regions (after amplification via ratio) where conventional RAW domain denoising methods [88] fail. These results validate the generalizability of our approach across different camera models and outdoor scenes. More visualization results can be found in the supplementary material.
4.4
Ablation Studies
In this section, provide ablation studies to demonstrate the effectiveness of our pipeline and different modules. The visualization results of ablated models are presented in the supplementary material.
RAW Domain Decoupling Strategy. To evaluate the effectiveness of our decoupling strategy for exposure correction and denoising, we compare UltraLED with the one-stage method that
8

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1nputibe1
RatinesMaamba
Zero
HDRUNNe
UltraLED (Cars)
Figure 4: Visualization results of different methods on the UHDR dataset. The methods labeled blue take long-exposure noiseless image input, the methods labeled red take short-exposure RAW image as input, and the methods labeled green take a short-exposure image RGB as input. UltraLED achieves good visual effects in both bright and dark regions. Note that there may be slight differences in our tone because our training data underwent a reversed ISP process. However, this difference is generally negligible and aligns with real-world conditions.
also operates in the RAW domain under identical settings. Additionally, to highlight the inherent advantages of the RAW domain in restoring UHDR scenes, we conduct experiments using the same settings in the RGB domain. The results are presented in Tab. 4
Brigh Noise and Ratio Map Encoding, To assess the contributions of the proposed brightness-awarc noise modeling and ratio map encoding, we conduct ablation studies on these two demonstrate that both components significantly enhance
modules. The results, as shown in Tab. | denoising performance and detail recovery UHDR scenes.
Table 4: Ablation on the image domain and the application of the decoupling strategy.
Table 5: Ablation on Ni. and Ratio Map Encoding.
Ratio
x50
x 100
X00
Ratio
x50
100
x000
Domian Decoupling PSNR/SSIM PSNR/SSIM PSNR/SSIM
No. Encading PSNR/SSIM PSNR/SSIM PSNR/SSIM
22.844/0.699 21,732/0.66 20.531/0.55
X
26.01 862. 25.814/0.838 24.51
RGB
22,937/0.663 688 21.56 11.566/0.613 20.376/0.554
X
v
26.301/00.8 878 26.005/0.849 24.678/0.766
25.13770.878
24.632/0.447
27.062/0.K6 882 8938000777111 26.734/0.851
RAW
27.598/08898
27.327/0.887 27.091/0.880
27.598/0.898 27.327/0.887 27.091/0.860
Input(x1)
Input(x10)
Input(x 100)
ELD
UltraLED (Ours)
Figure 5: Visual comparison on the SID 10 dataset. It is well-known that RGB-based methods [55 23 25 2 have limited ability to recover details in extremely low-light regions. Therefore, we only compare UltraLED with ELD [48] that is representative RAW-based approach. UltraLED not only performs effective denoising in the dark areas of the scene but also well preserves the details in bright regions, such as the textures of trees, windows, and wall surfaces under light.

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4.5 Limitations
Although UltraLED significantly outperforms RGB domain approaches in UHDR scenes, shares a common limitation with RAW denoising methods [48] 54 21]: the strong dependence of the noise model on camerf-specific parameters. We tested the performance of different cameras using their respective noise parameters on our UHDR dataset captured with the SonyA7Mr
demonstrate this more intuitively. The results shown in Tab. 6 indicate the impact of inaccurate noise parameters increases with increasing noise intensity. Consequently, for different camera types, the model needs to be retrained using noise parameters specifically calibrated for the type.
Table 6: Results of the cross-camera experiments.
x50 PSNR/SSIM
x100
x200
Camera Type
PSNR/SSIM
PSNR/SSIM
NikonD850 AonyA7S2 SonyA7M4 (matching)
26.425/0.866 26.333/0.866
25.943/0.825
24.784/0.735
26.035/0.854
25.587/0.
27.598/0.898
27.327/0.887
27.091/0.860
4.6
Controllable Local Exposure Correction
Since SID 10 most RAW domain denoising methods have achieved varying brightness levels by adjusting the amplification ratios of the input. In contrast, UltraLED applies the ratio primarily to the darkest regions of the image. For areas that are already bright, it adaptively adjusts local exposure, resulting in more natural appearance. Qualitative results demonstrating this capability are shown in Fig- 6 Notably, with global brightness adjustment alone, as illustrated the "Input" column, images tend to become overexposed, losing important highlight details. In contrast, our approach effectively preserves fine details and textures while progressively enhancing shadow regions as the amplification ratio increases. This indicates that UltraLED has controllable local exposure correction capability, providing users with a more diverse user experience.
Input(x1)
Input(x50)
UltraLEDxx50)
UltraLED(x100)
UltraLED(x200)
Figure 6: Visualization results on different amplification ratios of the initial RAW image in our model. Note that these images are all the same input multiplied only by different ratios. We only show the visualization results for the original input without amplification and the (x50) for the input.
Conclusion
For UHDR scenes, previous methods typically rely on multi-exposure fusion for image restoration. Single-frane frameffrmeeaaa approaches often struggle, either failing to recover details extremely dark regions or being unable to reconstruct severely overexposed areas. We propose a novel solution that operates the RAW domain by decoupling exposure correction and denoising. This enables the model to fully leverage the higher bit depth and simpler noise distribution of RAW images. Additionally, we introduce brightness- noise modeling and ratio map encoding to guide the recovery of image everything in the UHDR scene.
color and detail. With only single-frama RAW image,
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (62306153, 62225604), the Natural Science Foundation of Tianjin, China (25ZXRGGX00290, 24JCIQJC00020, 2SJCQNJC01390), the Young Elite Scientists Sponsorship Program by CAST (YESS20240686), the Fundamental Research Funds for the Central Universities (Nankai University, (0-63243113)) and Shenzhen Science and Technology Program (JCYJ202408) 131 14237048). The computational devices partly supported by the Supercomputing Center of Nankai University (NKSC).
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We do involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, not include any information that would break anonymity (if applicable), such the institution conducting the review.
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16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Answer: [No]
Justification: Our paper does not have content about LLMS.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to L.LM policy for what should or should not be described.
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Supplementary Material
This supplementary material provides additional insights and results supporting our main paper. We first elaborate on our concept of "see everything" and validate its effectiveness in UHDR scenes (Tab.
7 Fig. S We then assess the impact of different denoising networks (Tab. g and present detailed visual ablations highlighting the benefits of our RAW domain decoupling strategy (Fig. [0 Fig. I1 and noise modeling (Fig. [3, Fig. T4). Furthermore, we evaluate various ratio map encoding techniques and demonstrate the superiority of our proposed encoding both the UHDR and ELD datasets [48) (Tab. 9 Tab. TOP. Finally, we provide experimental settings, broader societal impact discussions, and license information.
Figure 7: The interface of our demo.
To further demonstrate the effectiveness of UltraLED, we ereated demo included in the supple- mentary materials. The interface shown in Fig. 7 We recorded short videos in UHDR scenes and processed them frame by frame to showcase their performance. These videos simulate real-world scenes where handheld camera captures scenes with both camera and object motion. Despite the movement, UltaLED can still recover all regions using short-exposure snapshot, demonstrating its practicality and convenience compared to multi-frame fusion approaches.
A More Detailed Explanation of "See Everything"
We define "see everything" as the ability to capture all regions' content in UHDR scenes, including the objects in extremely bright or dark regions, well as both moving and static elements. A comparison between UltraLED and previous approaches is shown in Tab. Compared with previous methods, UltraLED can "see" all regions in UHDR images.
Extremely Bright and Extremely Dark Regions. Previous single-frame methods struggle with this, particularly those methods 23 25 12
based the RGB domain, which are limited by bit depth and cannot simultaneously preserve rich details in both highlight and shadow areas. RAWebased methods [10 48 28 21 (4), while not restricted by bit depth, often fail to simultancously recover details across varying brightness levels and perform accurate exposure correction.
52 24
Moving and Static Regions. In comparison with the multi-frame HDR fusion techniques 14 36 51
17,, UltraLED has a clear advantage handling motion, especially for moving objects dark regions. As shown in Fig. 88 in scene where both the camera and the scene are in motion, even state-of-the-art diffusion-bases method [14 fails recover details in all regions, particularly moving objects in dark regions. Leveraging the higher potential of RAW images under low-light conditions, our
22

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Table Applicable Regions for Different Methods in UHDR Scenes.
Extremely Bright
Extremely Dark
Domain
Methods
Moving
Statie
Moving
Static
Short-exposure-basee Methods
[53
23
25
RGB
Long-exposure-based Methods
12
52
24
56
X
X
X
v
Multi-exposure-basee Methods
Denoising Methods
10
48
28
21
54
X
V
RAW
Ultrl.EED (Ours)
V
V
exnnssxx input
Short-exposure input
MARAUDERS MAP
UltraFusion
IE
UltraLED (Ours)
Figure 8: Visual comparison between UltraLED and UltraFusion 14 in UHDR scene with motion. Note that UltraLED uses short-exposure single-frame RAW image input, while UltraFusion takes a short-exposure RGB image and a long-exposure RGB image as inputs. UltraFusion struggles to recover details in dark moving regions and performs poorly in some highlight areas due to aligmment difficulties. In contrast, UltraLED, being single-frame and thus alignment-free, successfully restores details in dark regions while avoiding motion artifacts.
approach can recover fine details using only short exposure. This not only helps capture moving objects without motion blur but also avoids alignment issues, thanks to our single-frame design. These characteristics are especially crucial for dynamic scenes whether due camera shake or scene motion as well as for video applications.
B
User Study
We conducted user study to evaluate UltraLED, Specifically, we selected scenes from the UHDR dataset. Since the UHDR dataset mainly consists of indoor scenes, we also captured 11 outdoor UHDR scenes. addition, we included 15 UHDR scenes from the SID [10] dataset to assess performance different sensors. A total of 122 participants were invited to take part in the study. For each scene, users were asked to compare the results of our method with those of other
baseline methods [8 2 223. The results are shown as Fig. 9 which shows that users consistently
23

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preferred our method over others, indicating that UltraLED produces more visually pleasing images UHDR scenes. This aligns well with our quantitative evaluation.
UltraLED
Competitors
1
0.8
Voting Rate
0.4
0.2
Indoor Soenes
Outdoor Scenes
Figure 9: User study on different datasets.
C Impact of Different Denoising Networks
results. The results shown as Tab. [8] These experiments demonstrate that using more advanced network architectures [18 (53 enables the denoising network better learn the noise model, thereby further enhancing the
We also evaluated the impact of different denoising networks
performance of UltraLED, For fair comparison, we used U-Net [10] as the denoising network when comparing with other methods.
Table 8: Quantitative comparisons of using different denoising networks as the denoising backbone on the UHDR dataset.
x50 PSNR/SSIM
100
X00
Network
PSNR/SSIM
PSNR/SSIM
U-Net 10
27.598/0.898
27.327/0.887
27.091/0.Cm 1/0.80
ResUnet 18
27.741/0.904
27.533/0.80
27.288/0.883
Restormer 53
27.982/0.911
27.732/0.903
27.437/0.891
D Visualization Results of Ablation Studies
In this section, we present the visualization results corresponding the ablation studies in Sec. 4.4
RAW Domain Decoupling Strategy. The corresponding visualization results are illustrated in Fig. I0 and Fig. [1 Due to the limited bit depth and the complex noise distribution in the RGB domain, RGB-based methods exhibit significantly inferior performance compared to RAW-based methods. This discrepancy is particularly obvious in the recovery of darker regions under extremely high dynamic range scenes. RAW-basee methods can reconstruct more realistic details low- light areas, whereas RGB-based methods suffer from severe color distortion and, in extremely dark regions, often fail to recover meaningful details. In contrast, within the RAW domain, the strategy of decoupling exposure correction and denoising proves markedly beneficial for the restoration of both detail and color dark regions.
Brightness-Aware Noise and Ratio Map Encoding. The visual results of the ablation study on Brightness-aware Noise and Ratio Map Encoding are shown in Fig. T2 is evident that both modules contribute positively to noise reduction and detail restoration.

---

RAW (Ours)
RAW w/o Decoupling
Input
RGB
RGB w/o Decoupling
Ground Truth
Figure 10: Visualization the ablation experiment results for the RAW domain decoupling strategy. RAW/RGB indicates that the experiment is conducted in the RAW domain/RGB domain, and "w/o Decoupling" means that the decoupling strategy is not used. Pay attention to the details within the red box (Zoomed in and Brightened for Best View). It can be that in the RAW domain, the decoupling strategy significantly improves the recovery of details and denoising. The textures of portraits and building blocks clearer, while in the RGB domain, almost details can be recovered.
Brightness-Aware Noise particularly crucial scenarios with severe noise. Without incorporating this module, the output at x20 magnification exhibits significant color distortion, as demonstrated Fig. 13 and Fig. [4
E Ratio Map Encoding
To demonstrate the superiority of our encoding strategy over other methods, we conducted quantitative evaluations using alternative encoding approaches under the same experimental settings, as shown in Tab. [ The results indicate that our encoding consistently outperforms others across different ratio values. This because our encoding method is based on a Gaussian distribution, which better captures the continuity of the ratio as well as the correlations between different ratio values. Additionally, the weight represents the overall noise intensity, reinforcing the relationship between the ratio and the noise distribution.
25

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RAW (Ours)
RAW w/o Decoupling
Input
RGB
RGB w/o Decoupling
Ground Truth
Figure 11: Visualization of the ablation experiment results for the RAW domain decoupling strategy. RAW/RGB indicates that the experiment is conducted in the RAW domain/RGB domain, and "w/o Decoupling" means that the decoupling strategy is not used. Pay attention to the details within the red box (Zoomed in and Brightened for Best View). It can be seen that in the RAW domain, the decoupling strategy significantly improves the recovery of colors and denoising. The colors of the doll are more realistic with less noise, while in the RGB domain, almost details can be recovered.
Table 9: Quantitative evaluation results of different encoding methods the UHDR dataset. "None" means no encoding used. "Onehot" means one-hot encoding is used. and "Position" means position encoding is used. It can be observed that our encoding method provides the most significant performance improvement.
x55
x 100
x200
Encoding
PSNR/SSIM
PSNR/SSIM
PSNR/SSIM
None
27.062/0.882
26.893/0.871
26.734/0.851
Onehot
27.463/0.897
27.201/0.877
26.982/0.856
Position
26.893/0.877
27.160/0.872
26.781/0.857
UltraLED (Ours)
27.598/0.898
27.327/0.887
27.091/0.860
Additionally, further showcase the effectiveness and generalization capability of our encoding strategy in guiding denoising under varying brightness levels, we directly tested its impact on denoising performance at different ratios in the RAW domain using the ELD dataset [88] Since we focused solely on denoising performance and the dynamic range of scenes in the ELD dataset 48 is relatively narrow, each image only requires a single ratio value. worth noting that, after normalization, the pixel values of low-light images in the RAW domain are typically smaller than those in the RGB domain-ofien falling within range like [0, which tends to result in lower
mean squared error and higher PSNR. The quantitative results shown Tab. 10
To further
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hulpyy anaa satitndull Syturo
bappy sacesful Euturro
UltraLED (Ours)
Ours w/o Encoding
Input
famil and
huppy family and sucoosdull future.
Ours w/o Nos
Ours w/o Encoding & w/o Nba
Ground Truth
Figure 12: Visualization results of the ablation experiment of Ratio Map Encoding and Noa- "w/o Encoding" indicates the case without using Ratio Map Encoding, and "w/oNNa. represents the situation without using Nia noise. By observing the changes in the letters, it can be seen that both modules play positive role in the recovery of details.
UltraLED (Ours)
Ours w/o Encoding
Input
Ours w/o No..
Ours w/o Encoding & w/o Noa
Ground Truth
Figure 13: Visualization results of the ablation experiment for Ratio Map Encoding and Nim under strong noise( x 200). "w'o Encoding" indicates the case without using Ratio Map Encoding, and "w/o Nr.. represents the situation without using No. noise. Pay attention to the differences between the methods with and without using No noise. The result the method without using Noa noise turns obviously purple under strong noise, which seriously affects the visual effect of the image.
demonstrate the generalization capability of our encoding strategy different camera sensors, we evaluate it on two different ELD subsets SonyA752 and NikonD850, with ELD noise model. The results are shown as Tab. [1]
Experimental Environments
In our implementations, we used a single NVIDIA GeForce RTX 3090 GPU (24 GB), paired with 20-core CPU and 64 GB of RAM for training and testing. The Torch version is 1.13.1, and the CUDA version is 12.2. Thanks the use only two U-Net architectures, inference on 4680 RAW image takes approximately 0.85 seconds.
27

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UltraLED (Ours)
Ours w/o Encoding
Input
Ours w/o Noo.
Ours w/o Encoding & w/o Nba
Ground Truth
Figure 14: Visualization results of the ablation experiment for Ratio Map Encoding and Ni. under strong noise( 2200). "w/o Encoding" indicates the case without using Ratio Map Encoding, and "w/o Noa represents the situation without using Noa noise. Pay attention to the differences between the methods with and without using Noc noise. The result of the method without using Nos noise turns obviously purple under strong noise, which seriously affects the visual effect of the image.
Table 10:
Quantitative evaluation results of different denoising methods the ELD 48 dataset. It can be observed that our encoding significantly improves the performance on PG, ELD SERN (54, especially under brighter conditions.
and
x1 PSNR/SSIM
x10
x100
x200 PSNR/SSIM
Method
PSNR/SSIM
PSNR/SSIM
PG
54.974/0.999 998
51.228/0.99 992
42.029/0.885 885
38.0 $8.059/0.800
PG+Encoding
56.032/0.999
51.593/0,993
42.132/0.887
38.413/0.808
ELD EnD+Encoding
53.092/0.999 997
50.940/0.994
45.529/0.973
43.010/0.944
54.141/0.998
51.375/0.995
45.646/0.975
43.079/0.951
SFRN
$3.478/0.997
51.231/0.992
45.631/0.97 977
43.013/0.947
SFRN+Encoding
54.662/0.998
51.805/0.993
45.841/0.980
43.021/0.947
G
Broader Impacts
Potential Positive Impacts. UltraLED enhances image quality. enabling users to capture high- quality night scenes without relying on professional equipment. This improvement may also benefit surveillance cameras by providing better performance in low-light conditions, potentially contributing public safety. Furthermore, it could drive advancements in related industries such smartphone cameras and security systems, and even support fields like astrophotography and scientific research.
Potential Negative Impacts. While enhanced nighttime surveillance can offer security benefits, also raises potential privacy concerns. not properly regulated, the technology might be used in
Table 11: Quantitative evaluation results of different sensors the ELD
48 dataset.
x1
x10
x100
x200
Method
PSNR/SSIM
PSNR/SSIM
PSNR/SSIM
PSNR/SSIM
SonyA7S2
53.092/0.997
50.940/0.994
45.529/0.973
43.010/0.947
SanyA7S2+Encofiii
54.141/0.998
51.375/0.995
45.646/0.975
43.079/0.951
NikonD850
50.673/0.99.
48.763/0.999
43.991/0.966
41.821/0.94 940
EikonDS50+Encoding
52.012/0.995
49.237/0.991
44.178/0.969
41.905/0.941
28

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BA RR
BE BE
UltraLED
HDRTVNet++
ELD
48
RetinexMamba 2
Figure 15: More visualization results on the UHDR dataset.
UltraLED
HDRTVNet++
ELD 4%
RetinexMamba
Figure 16: More visualization results on the UHDR dataset.
ways that could unintentionally affect individual privacy, such as in certain forms of unauthorized monitoring or photography.
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H Licenses for Existing Assets
UltraLED implemented in the style of BasicSR 47 which is an open source image and video restoration toolbox for super-resolution, denoising, deblurring, etc. We encourage further research based on this codebase. The training data we use is sourced from RawHDR [57]. To synthesize signal-dependent and signal-independerr noise in the RAW domain, we adopt the noise model from
ELD 48
I More Visualization Results
More visual comparison results presented in Fig. 15 and Fig. 16 To demonstrate the generality of UltraLED, we also captured and processed images in daytime UHDR scenes. The visual results are shown in Fig. 17
Input
Output
Figure 17: Visual Results of Processed Daytime Scenes.
30


Reviews:
[{"review_id": "XQparKJ0ko", "reviewer": "Reviewer_N566", "summary": "This paper studies reconstruction of ultra-high dynamic range scenes from a single short-exposure RAW image. It proposes a two-stage pipeline—first predicting a pixel-wise ratio map for adaptive exposure correction, then applying a brightness-aware noise model to guide denoising—fully leveraging RAW’s high bit depth. Contributions include (1) decoupling exposure and noise restoration, (2) a novel data synthesis workflow combining simulated over-exposure, HDR fusion, and realistic noise modeling, and (3) a purpose-built UHDR dataset.", "questions": "Could you include experiments on scenes with motion (e.g., moving objects or camera motion) and compare against a state-of-the-art multi-frame fusion method such as UltraFusion?", "limitations": "None", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n- The paper is technically sound, with a clear problem formulation and well-justified two-stage design that decouples exposure correction from denoising.\n\n- The motivation for single-frame RAW-domain UHDR reconstruction is crisply presented: avoiding misalignment and ghosting in dynamic scenes.\n\n- Demonstrates that a single short-exposure RAW frame suffices to recover both highlights and deep shadows, a capability critical for real-world night and mixed-lighting scenarios.\n\nWeaknesses\n\n- The core concern is that the paper’s main claim—single-frame RAW reconstruction avoids the ghosting artifacts of multi-frame exposure fusion in dynamic scenes—can only be validated by directly comparing against strong multi-frame fusion baselines (e.g., UltraFusion). Without such head-to-head experiments, the asserted advantage of using a single frame remains unproven and the contribution loses its significance.\n\n- The use of two UNets and multi-step data synthesis may incur higher runtime or memory requirements than existing single-stage RGB-domain approaches; a detailed analysis of inference speed and model footprint is missing.\n\n[1] UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your valuable comments and suggestions.\n\n# 1 **Comparison with Multi-frame Method**\n\nSince the ground truth for multi-frame methods and single-frame methods is not easily unified, **we conducted a user study head-to-head comparing our approach with UltraFusion** [1]. Specifically, we captured 18 scenes from everyday environments, primarily urban night scenes under ultra-high dynamic range (UHDR) conditions. Our method utilized only a single short-exposure RAW image captured at ISO 200 and 1/400s shutter speed, while UltraFusion leveraged both an RGB image with the same exposure and an additional EV0 RGB image for fusion.\n\nAll scenes were captured handheld, introducing natural camera shake, and some included dynamic elements such as moving pedestrians or vehicles. In addition, this dataset was also used to compare our results with ELD [2], another single-frame RAW-based method. A total of 122 participants evaluated the results, and 83% of the images processed by our method were preferred. The following table summarizes the percentage of votes each method received:\n\n| Method | ELD  | UltraFusion | Ours |\n| :----: | :--: | :---------: | :--: |\n| Winner |  6%  |     11%     | 83%  |\n\nIn regions affected by camera motion or object movement, where alignment across frames becomes unreliable, UltraFusion often produces results with noticeable texture loss or unrealistic details. In contrast, our method is capable of faithfully reconstructing fine details and preserving complete textures. **This advantage is clearly illustrated in the comparison with UltraFusion shown in Figure 2 of the appendix in our supplementary material**. Furthermore, in cases where images at EV0 exhibit significant motion blur, our method—relying solely on short-exposure inputs—is still able to recover sharp representations of moving objects. UltraFusion, on the other hand, tends to retain motion blur artifacts in these regions due to its reliance on long-exposure frames. \n\n**We will make the RAW images and results from our user study publicly available upon the paper's review results to ensure transparency and reproducibility**.\n\n\n# 2 **Inference Speed and Model Memory**\n\nRegarding the concern over computational and memory efficiency, our approach benefits from a lightweight, decoupled architecture, which gives us a distinct advantage compared to existing methods. For a fair comparison, all RGB inputs were resized to half their original resolution, matching the scale of our RAW inputs (which are converted to four-channel inputs at half resolution based on the bayer pattern).\n\nThe table below reports the average inference time, parameters for processing single-frame 7028×4688 RAW images (Ours) or 3514×2344 RGB images (Other Methods) on the same GPU:\n\n|    Method    | Inference Time(s) | Parameters(M) |\n| :----------: | :---------------: | :-----------: |\n| HDRTVNet [3] |       1.09        |     1.59      |\n| RetinexMamba [4] |       1.58        |     4.41      |\n| FluxFill [5] |      142.13       |       —       |\n| UltraFusion  |      115.26       |    1774.08    |\n|     Ours     |       1.07        |     16.24     |\n\nAs shown in the table, owing to the relatively simple architecture of our network, our method achieves a faster inference speed compared to HDRTVNet and RetinexMamba, and significantly outperforms UltraFusion and FluxFill in this regard. While the parameters in our model is substantially lower than that of UltraFusion, it is larger than those of RetinexMamba and HDRTVNet. Nonetheless, considering the superior performance achieved by our method, the increase in parameters is well justified.\n\n\n# 3 Discussion on the Fundamental Value of Single-Frame Methods\n\n Beyond the inherent limitations of multi-frame methods—such as their sensitivity to motion and difficulty in aligning dynamic content—single-frame approaches possess unique and irreplaceable advantages in a range of real-world applications. For instance, scenarios involving flash photography, high-speed imaging, or event-driven capture often permit only a single exposure, making multi-frame acquisition infeasible or even impossible.\n\nIt is therefore inappropriate to dismiss the significance of single-frame methods simply because multi-frame alternatives may perform well under controlled or static conditions. Single-frame approaches address a fundamentally different set of practical constraints, and the ability to reconstruct high-quality results from a single short-exposure input is essential for enabling reliable performance in time-critical or motion-rich environments.\n\nIn this context, our work not only contributes to the advancement of single-frame HDR reconstruction from RAW images, but also reinforces the broader relevance and necessity of research in this direction.\n\n\n\n[1] UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion\n\n[2] Physics-based Noise Modeling for Extreme Low-light Photography\n\n[3] A New Journey from SDRTV to HDRTV\n\n[4] Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement\n\n[5] Flux. https://github.com/black-forest-labs/flux"}, {"review_id": "I9SAP66HSs", "reviewer": "Reviewer_2iPX", "summary": "This papers presents a new single-shot method for capturing UHDR content. The method relies on heavily underexposed RAW images, which are then adaptively exposed based on new learned ratio maps, and brightness-aware denoising. The results are of very high quality, translating naturally to video as well. The networks used are simple and efficient, making the method highly practical. The quantitative and qualitative comparisons clearly show the superiority of the method, and the ablations demonstrate the effectiveness of the whole approach.", "questions": "Figure 3 in the paper shows more colorful results than the ground truth, which could be in principle more desirable, but at the same time it can introduce some inconsistencies. For example, in the last row, the ground truth shows an evenly desaturated green mouth region, while the produced result has a more uneven distribution. Is this coming from the use of standard isp and exposure fusion for the tone mapping? Could there be some extra steps to mitigate it?", "limitations": "yes", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "- Strengths:\n- A very practical method that checks all the boxes compared with previous works.\n- Simple and effective 2-step approach that makes the problem very tractable.\n- Very high quality results, way ahead of previous works.\n- Clear presentation of problem, context, method and evaluations.\n- A carefully created UHDR dataset that may help future research in this space.\n\n- Weaknesses:\n- As the authors acknowledge, different cameras may have different noise profiles, which would require retraining the noise models. However, it is would be very feasible for different vendors to train those modules themselves.", "was_revised": true, "final_justification": "The authors replied to my minor comments. I have read the other reviews and discussion, and I’m still keeping my original score. I believe the quality and elegance of the solution is worth acceptinng.", "rebuttal": "Sincerely thank you for your recognition of our work.\n\n# 1 About Inconsistent Tones\n\nYes, the difference you mentioned primarily arise from the inverse ISP process employed during exposure fusion for synthetic data generation. The tone and style of an image are closely related to the specific preferences of ISP adopted by different vendors. For manufacturers, it is relatively straightforward to adjust the tonal rendering to match their desired aesthetic. As a RAW-to-RAW method, our goal is to demonstrate the feasibility and effectiveness of our approach, and thus we intentionally employ a minimal and generic ISP pipeline without incorporating any vendor-specific tuning or stylistic enhancement.\n\n\n\n# 2 Generalization to Different Cameras\n\nRegarding the weakness, while it is true that our model requires retraining for different sensor types, the cost of retraining for different manufacturers is relatively low. Moreover, as derived in Equation (4), **our brightness-aware noise $N_{ba}$ is designed to adapt well to any noise model associated with any camera**. The primary variation between noise models lies in the modeling of the signal-independent noise $N_{in}$: for instance, the PG model uses a Gaussian distribution, the ELD [4] model combines Gaussian and Tukey-lambda mixture distributions, and the SFRN [5] model samples real noise from dark frames.\n\nImportantly, **our ratio map encoding module consistently enhances denoising performance across various types of camera sensors and noise models**.\n\nWe have already provided thorough evidence of its effectiveness on the SonyA7M4 sensor within our paper. To further demonstrate the generalization capability of our method across different camera sensors, we evaluate it on two additional ELD subsets—SonyA7S2 and NikonD850—with ELD noise model:\n\n|                      |        ×1        |       ×10        |       ×100       |       ×200       |\n| :------------------: | :--------------: | :--------------: | :--------------: | :--------------: |\n|                      |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |\n|       SonyA7S2       |   53.092/0.997   |   50.940/0.994   |   45.529/0.973   |   43.010/0.947   |\n| SonyA7S2 + Encoding  | **54.141/0.998** | **51.375/0.995** | **45.646/0.975** | **43.079/0.951** |\n|      NikonD850       |   50.673/0.993   |   48.763/0.990   |   43.991/0.966   |   41.821/0.940   |\n| NikonD850 + Encoding | **52.012/0.995** | **49.237/0.991** | **44.178/0.969** | **41.905/0.941** |\n\nIn addition, to validate the robustness of our method under different noise modeling assumptions, we conduct experiments on the Sony subset of the ELD dataset using three distinct noise models: a simple parametric Gaussian model (PG), a physically calibrated noise model (ELD), and a data-driven real-world noise model (SFRN). The results further confirm the adaptability and generalization strength of our approach:\n\n|                 |        ×1        |       ×10        |       ×100       |       ×200       |\n| :-------------: | :--------------: | :--------------: | :--------------: | :--------------: |\n|                 |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |\n|       PG        |   54.974/0.998   |   51.228/0.992   |   42.029/0.885   |   38.059/0.802   |\n|  PG + Encoding  | **56.032/0.999** | **51.593/0,993** | **42.132/0.887** | **38.413/0.808** |\n|       ELD       |   53.092/0.997   |   50.940/0.994   |   45.529/0.973   |   43.010/0.947   |\n| ELD + Encoding  | **54.141/0.998** | **51.375/0.995** | **45.646/0.975** | **43.079/0.951** |\n|      SFRN       |   53.478/0.997   |   51.231/0.992   |   45.631/0.977   |   43.013/0.947   |\n| SFRN + Encoding | **54.662/0.998** | **51.805/0.993** | **45.841/0.980** | **43.021/0.947** |\n\nThese results clearly demonstrate that our framework is robust and generalizable to different camera sensors and noise models.\n\n\n\n[1] Beyond learned metadata-based raw image reconstruction\n\n[2] Unprocessing Images for Learned Raw Denoising\n\n[3] Metadata based raw reconstruction via implicit neural functions\n\n[4] Physics-based Noise Modeling for Extreme Low-light Photography\n\n[5] Rethinking Noise Synthesis and Modeling in Raw Denoising"}, {"review_id": "m6b0aJsV8j", "reviewer": "Reviewer_UcfZ", "summary": "This paper presents a method for reconstructing ultra-high dynamic range (UHDR) images from a single short-exposure RAW image. The proposed two-stage framework performs exposure correction and brightness-aware denoising to recover details in dark regions. A custom dataset with 9-stop bracketing is used for training, and experiments show significant improvements over existing single-frame methods.", "questions": "Is this the first paper to address HDR reconstruction from a single short-exposure RAW image?\n\nIn Section 4.5, the method shows good performance only on specific camera types, but fails to generalize to others, which is a significant limitation. Could the authors provide some experimental results to demonstrate how poorly the model performs on different types of cameras without retraining?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The proposed method achieves excellent performance, as shown in Tables 1, 2, and 3, outperforming previous methods such as HDRTVNet, RetinexMamba, and others.\n\nIn addition, the authors also build a corresponding dataset for benchmarking UHDR reconstruction performance. However, the synthesized UHDR images lack thorough validation, making it uncertain whether they can accurately evaluate the model's performance.\n\nMoreover, the writing quality of the paper is quite poor, and it's difficult to clearly understand the contributions. Is the paper merely proposing a data synthesis pipeline? The network architecture seems to be entirely based on previous methods.\n\nAs mentioned in Section 4.5, the strong dependence of the noise model on camera-specific parameters makes it difficult to apply the method in real-world scenarios.", "was_revised": true, "final_justification": "The authors' rebuttal has addressed my concerns. Therefore, I have decided to raise my rating.", "rebuttal": "We are sincerely grateful for your valuable comments and suggestions.\n# 1 Novelty and Practical Relevance\n\n**Yes,** **our work is the first to leverage a short-exposure RAW image for HDR reconstruction.** Also, **it is the first to jointly address the challenges of denoising and HDR tone mapping from short-exposure RAW input**—a long-standing issue highlighted by SID [1] but left unsolved for years. In the discussion section of the original SID paper, the authors noted the difficulty of simultaneously removing noise and performing HDR tone mapping from short-exposure RAW data.\n\nSubsequent research in this domain has largely focused on developing more accurate or more accessible noise modeling techniques (e.g., ELD [2], SFRN [3], PMN [4], LED[5], LLD[6], PNNP[7]). However, our method is the first to effectively tackle this dual challenge, as shown in Figure 4 of our paper. This advancement holds strong practical significance: in UHDR scenes, it is common to encounter camera shake or dynamic elements (e.g., moving objects). Our method enables high-fidelity HDR reconstruction even from extremely short exposures—something that no prior approach has been able to achieve.\n\n\n\n# 2 Experiments using non-matching cameras without retraining\n\nThe concern you raised likely stems from a misunderstanding of RAW-domain processing. In practice, each camera exhibits a unique noise distribution in the RAW domain. Fortunately, **many well-established techniques** [2-7] **exist to estimate a camera’s noise model and parameters**, and they are capable of producing reliable results. Consequently, it is rare—both in academia and industry—for RAW image processing to be conducted using inaccurate noise models without retraining.\n\nNevertheless, in response to your question, we present cross-camera experiments on our dataset (captured with SonyA7M4). In these experiments, **we applied noise parameters from non-matching camera models to test our dataset**:\n\n|  Camera Type |     ×50      |     ×100     |     ×200     |\n| :-------: | :----------: | :----------: | :----------: |\n|           |  PSNR/SSIM   |  PSNR/SSIM   |  PSNR/SSIM   |\n| NikonD850 | 26.425/0.863 | 25.943/0.825 | 24.784/0.735 |\n| SonyA7S2  | 26.333/0.868 | 26.035/0.854 | 25.587/0.796 |\n| SonyA7M4 (matching)  | **27.598/0.898** | **27.327/0.887** | **27.091/0.860** |\n\nIt can be seen that the impact of inaccurate noise parameters increases with increasing noise intensity. \n\n\n# 3 Generalization to Different Cameras\n\nBased on the derivation in Equation (4) in our paper, **our brightness-aware noise $N_{ba}$ can adapt well to any camera-specific noise model**. The variation across noise models primarily lies in different modeling of the signal-independent noise $N_{in}$. For example, PG assumes a Gaussian distribution; ELD [2] uses a mixture of Gaussian and Tukey-lambda distributions; and SFRN [3] relies on real dark frame sampling.\n\n**Our ratio map encoding module consistently enhances denoising performance across various types of camera sensors and noise models**.\n\nWe have already provided thorough evidence of its effectiveness on the SonyA7M4 sensor within our paper. To further demonstrate the generalization capability of our method across different camera sensors, we evaluate it on two additional ELD subsets—SonyA7S2 and NikonD850—with ELD noise model:\n\n|                      |        ×1        |       ×10        |       ×100       |       ×200       |\n| :------------------: | :--------------: | :--------------: | :--------------: | :--------------: |\n|                      |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |\n|       SonyA7S2       |   53.092/0.997   |   50.940/0.994   |   45.529/0.973   |   43.010/0.947   |\n| SonyA7S2 + Encoding  | **54.141/0.998** | **51.375/0.995** | **45.646/0.975** | **43.079/0.951** |\n|      NikonD850       |   50.673/0.993   |   48.763/0.990   |   43.991/0.966   |   41.821/0.940   |\n| NikonD850 + Encoding | **52.012/0.995** | **49.237/0.991** | **44.178/0.969** | **41.905/0.941** |\n\nIn addition, to validate the robustness of our method under different noise modeling assumptions, we conduct experiments on the Sony subset of the ELD dataset using three distinct noise models: a simple parametric Gaussian model (PG), a physically calibrated noise model (ELD), and a data-driven real-world noise model (SFRN). The results further confirm the adaptability and generalization strength of our approach:\n\n|                 |        ×1        |       ×10        |       ×100       |       ×200       |\n| :-------------: | :--------------: | :--------------: | :--------------: | :--------------: |\n|                 |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |\n|       PG        |   54.974/0.998   |   51.228/0.992   |   42.029/0.885   |   38.059/0.802   |\n|  PG + Encoding  | **56.032/0.999** | **51.593/0,993** | **42.132/0.887** | **38.413/0.808** |\n|       ELD       |   53.092/0.997   |   50.940/0.994   |   45.529/0.973   |   43.010/0.947   |\n| ELD + Encoding  | **54.141/0.998** | **51.375/0.995** | **45.646/0.975** | **43.079/0.951** |\n|      SFRN       |   53.478/0.997   |   51.231/0.992   |   45.631/0.977   |   43.013/0.947   |\n| SFRN + Encoding | **54.662/0.998** | **51.805/0.993** | **45.841/0.980** | **43.021/0.947** |\n\nThese results clearly demonstrate that our framework is robust and generalizable to different camera sensors and noise models.\n\n\n\n[1] Learning to See in the Dark\n\n[2] Physics-based Noise Modeling for Extreme Low-light Photography\n\n[3] Rethinking Noise Synthesis and Modeling in Raw Denoising\n\n[4] Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling\n\n[5] Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising\n\n[6] Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography\n\n[7]  Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising"}, {"review_id": "eyRmRKqCkj", "reviewer": "Reviewer_e9Nm", "summary": "This paper proposes a method for achieving ultra-high dynamic range (UHDR) imaging from a single short-exposure RAW image. The core insight lies in the observation that RAW images possess higher bit depth and more predictable noise characteristics compared to processed images. As a result, even short-exposure RAW captures retain comprehensive scene information, offering substantial potential for low-light image enhancement. The proposed pipeline consists of three main stages: (1) a U-Net architecture is used to predict a ratio map ; (2) this ratio map  guides the generation of a well-exposed image; and (3) a lightweight U-Net is subsequently trained to denoise the generated image.", "questions": "1.Please refer to weaknesses.\n2.In the comparative analysis, the authors highlight the challenges faced by single-image HDR methods that rely on RGB inputs, implicitly emphasizing the advantages of their single-frame RAW-based approach. However, this comparison may not be entirely fair. RAW images inherently contain significantly more information than RGB images, which are typically post-processed and compressed. As such, it is questionable whether comparing a single RAW image to a single RGB image provides a balanced evaluation. In fact, a single RAW image often has a larger file size and richer content than several RGB frames combined. This raises the question of whether a more appropriate baseline would involve multiple RGB images rather than just one.", "limitations": "Refer to weaknesses and questions, please add relevant explanations.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths：\nThe author summarizes the problems faced by traditional RGB-based HDR imaging methods and proposes a solution based on RAW image format, providing a new solution to related problems.\n\nWeaknesses：\n1.The novelty of this paper is limited. The core components of the proposed method are directly derived from prior work. For example, in Section 3.2, the key component of the method—the ratio map —is adopted from [9] (line 172). In Section 3.1, the strategy for synthesizing artificial overexposure is taken from [38]; the HDR fusion technique is based on [3]; and the noise modeling approach relies on [2].\n2.The references, baseline methods, and even the methodological choices are outdated. The authors are encouraged to incorporate more recent studies from the past few years to ensure the timeliness and relevance of their research.\n3.The simulation of artificial overexposure in Section 3.1 involves several handcrafted operations, such as adding random highlight patches, using bilateral filtering, and applying Gaussian blur to mimic light diffusion. However, the authors do not provide sufficient details regarding the parameter settings, the underlying rationale for these operations, or any analysis of their impact on the final imaging results.", "was_revised": true, "final_justification": "The authors' response has addressed most of my concerns, and I decide to raise my final score.", "rebuttal": "We sincerely thank you for your suggestions. However, we would like to respectfully **clarify several misunderstandings** that may have arisen due to limited familiarity with RAW-domain denoising and noise modeling, and further **elaborate on the key novelty of our method compared to prior works**.\n\n# 1 Clarifications on Misunderstandings\n\n## 1.1 Ratio Map Is Our Novel Contribution\n\nContrary to the assumption, **the concept of the *ratio map* does not originate from SID** [1]. Instead, it is an original component of our method, designed for local exposure adjustment and to guide the denoising process. Since SID, most research efforts on short-exposure RAW images have primarily focused on denoising alone (e.g., ELD [2], SFRN [3], PMN [4], LED [5], LLD [6], PNNP [7]). These methods apply a *global (image-wise)* amplification ratio—calculated from the exposure settings (ISO and shutter speed) of the ground-truth and short-exposure images—resulting in the same ratio applied across all pixels.\n\nThis leads to overexposure or underexposure in areas with non-uniform brightness distribution, as is evident in many examples in the SID dataset. We illustrate such cases in Figure 4 of our paper. In contrast, our pixel-wise *ratio map* enables **spatially adaptive exposure control**, allowing local modulation of brightness, as shown in Figure 5 of our paper.\n\n## 1.2 Our Noise Model Is Not Simply Adopted from ELD\n\nOur proposed brightness-aware noise $N_{ba}$ is not a direct application of the ELD noise model. Instead, it is a general implicit noise model that accommodates **any** noise model influenced by exposure adjustment, as theoretically proven in Equation (4). The primary difference between different noise models lies in how the signal-independent noise $N_{in}$ is characterized, taking PG, ELD and SFRN as examples:\n\n- PG assumes a Gaussian distribution,\n- ELD uses a mixture of Gaussian and Tukey-lambda distributions,\n- SFRN leverages empirical distributions sampled from dark frames.\n\nIn all cases, our *ratio map encoding* module significantly enhances the model's denoising capability. This is validated in our experiments on the ELD model (see appendix in our supplementary material Table 4). Additional results on the ELD SonyA7S2 subset using both PG and SFRN noise models are shown below:\n\n|               |        ×1        |       ×10        |       ×100       |       ×200       |\n| :-----------: | :--------------: | :--------------: | :--------------: | :--------------: |\n|               |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |    PSNR/SSIM     |\n|      PG       |   54.974/0.998   |   51.228/0.992   |   42.029/0.885   |   38.059/0.802   |\n|  PG + Encoding  | **56.032/0.999** | **51.593/0,993** | **42.132/0.887** | **38.413/0.808** |\n|     SFRN      |   53.478/0.997   |   51.231/0.992   |   45.631/0.977   |   43.013/0.947   |\n| SFRN + Encoding | **54.662/0.998** | **51.805/0.993** | **45.841/0.980** | **43.021/0.947** |\n\n**We emphasize the ELD model because it is widely adopted in both academia and industry, it can be easily replaced by any noise models.** Our framework is flexible and agnostic to the specific noise formulation.\n\n## 1.3 Realism of Synthetic Overexposure\n\nBeyond using Flare7K [8] to simulate overexposure, we introduced an additional strong degradation model that accounts for the complexity of brightness irregularities in low-light environments. In such scenes, intensity variation is often not caused by direct light sources but by complex interactions such as reflection and scattering. As mentioned in works like *IClight* [9], given the impracticality of precisely modeling all degradations, we adopt a broader model to better capture real-world variability. And its parameter settings will be released along with the code.\n\n\n\n# 2 On the Question of Novelty\n\nWe respectfully disagree with the assessment that our method lacks novelty. To the best of our knowledge, **this is the first work to reconstruct HDR images from short-exposure RAW inputs**, and more importantly, the **first to simultaneously address both denoising and HDR tone mapping** for such inputs. This is a challenge explicitly raised by the authors of the SID [1] in their *discussion* section, yet it has remained unsolved for years.\n\nPrevious studies have mainly focused on improving the precision or usability of noise modeling (e.g., ELD [2], SFRN [3], PMN [4], LED [5], LLD [6], PNNP [7]), without addressing the joint exposure correction and denoising problem. Our method fills this gap, as evidenced by Figure 4 in our paper.\n\nThe practical significance of this contribution is considerable: for UHDR scenes with camera shake or object motion, acquiring multiple frames is often infeasible. Our method enables accurate HDR reconstruction from a **single short-exposure RAW frame**, achieving a capability that no existing approach has demonstrated.\n\n**We believe it is unfair to claim our work lacks novelty when it successfully solves a long-standing, well-recognized problem in the field**.\n\nEven when compared to state-of-the-art methods such as *RetinexMamba* [10] or other representative baselines, our method shows clear advantages in challenging UHDR conditions.\n\n\n\n# 3 On Fairness of Comparisons\n\n**We have conducted fair comparisons with RAW-based denoising** [2, 5] **followed by image reconstruction**, which is the most reasonable strategy for UHDR recovery from single-frame RAW input for previous methods (the way they are synthesised after denoising is the same as our ground truth). Our proposed $N_{ba}$ and *ratio map encoding* modules enable us to outperform such baselines within the RAW domain itself, as shown in Table 2 of our paper.\n\nRegarding the concern that single-frame RAW is not meaningful unless compared to multi-frame RGB methods, we respectfully disagree. **There are many real-world scenarios where multi-frame capture is not possible**, such as flash photography or fast motion. This is demonstrated in our supplementary demo scenes, where multi-frame methods often fail due to motion artifacts.\n\nMoreover, every RGB image is ultimately derived from a RAW image. Thus, **RAW-based single-frame recovery is not only meaningful but sometimes the only viable solution**. Our work is aimed precisely at solving this class of problems.\n\nDespite this, we still performed a direct comparison through a user study with *UltraFusion*, a state-of-the-art multi-frame RGB-based HDR method. We captured 18 real-world UHDR scenes (mainly urban night scenes). Our method only used RAW images captured at ISO 200, 1/400s shutter, while UltraFusion fused two RGB frames: the same short exposure and an additional EV0 exposure.\n\nAll data was captured handheld, introducing mild camera shake and natural motion (e.g., pedestrians, vehicles). A total of 122 users participated in the evaluation. Additionally, we also included comparisons with ELD, another single-frame RAW method. The results indicate that 83% of our results were preferred. The results are as follows: \n\n| Method | ELD  | UltraFusion | Ours |\n| :----: | :--: | :---------: | :--: |\n| Winner |  6%  |     11%     | 83%  |\n\nIn regions affected by camera motion or object movement, where alignment across frames becomes unreliable, UltraFusion often produces results with noticeable texture loss or unrealistic details. In contrast, our method is capable of faithfully reconstructing fine details and preserving complete textures. **This advantage is clearly illustrated in the comparison with UltraFusion shown in Figure 2 of the appendix in our supplementary material**. Furthermore, in cases where images at EV0 exhibit significant motion blur, our method—relying solely on short-exposure inputs—is still able to recover sharp representations of moving objects. UltraFusion, on the other hand, tends to retain motion blur artifacts in these regions due to its reliance on long-exposure frames. \n\n**We will make the RAW images and results from our user study publicly available upon the paper's review results to ensure transparency and reproducibility**.\n\n\n[1] Learning to See in the Dark\n\n[2] Physics-based Noise Modeling for Extreme Low-light Photography\n\n[3] Rethinking Noise Synthesis and Modeling in Raw Denoising\n\n[4] Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise Modeling\n\n[5] Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising\n\n[6] Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography\n\n[7] Physics-guided Noise Neural Proxy for Practical Low-light Raw Image Denoising\n\n[8] Flare7K: A Phenomenological Nighttime Flare Removal Dataset\n\n[9] Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport\n\n[10] Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement"}]

Author rebuttal:

Meta-review: This paper received four mixed, but largely positive-leaning reviews. 

There was general appreciation for the motivation and significance of the problem considered here, the technical soundness and practicality of the approach, and the quality of the results.


There were some concerns raised in the initial reviews regarding experimental evaluation (strength of baseline comparisons) and presentation quality. There was significant author-reviewer discussions following the initial review period, after which three of the reviewers converged to an accept rating (1 Strong Accept, 2 Borderline Accepts). The fourth reviewer (with an initial borderline reject rating) did not participate in the discussions, nor updated their final rating or respond to the author rebuttal. 

Therefore, on balance, an accept decision was reached.
Decision: Accept (poster)