DeAltHDR: Learning HDR Video Reconstruction from Degraded Alternating Exposure Sequences
Authors: Shuohao Zhang
Zhilu Zhang
RongJian Xu
Xiaohe Wu
Wangmeng Zuo
Venue: ICLR 2026 Poster (2026)
Keywords: HDR Video Reconstrucion
Alternating Exposures
Degraded Sequences
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: High dynamic range (HDR) video can be reconstructed from low dynamic range (LDR) sequences with alternating exposures. However, most existing methods overlook the degradations (e.g., noise and blur) in LDR frames, focusing only on the brightness and position differences between them. To address this gap, we propose DeAltHDR, a novel framework for high-quality HDR video reconstruction from degraded sequences. Our framework addresses two key challenges. First, noisy and blurry content complicate inter-frame alignment. To tackle this, we propose a flow-guided masked attention mechanism that leverages optical flow for a dynamic sparse cross-attention computation, achieving superior performance while maintaining efficiency. Notably, its controllable attention ratio allows for adaptive inference costs. Second, the lack of real-world paired data hinders practical deployment. We overcome this with a two-stage training paradigm: the model is first pre-trained on our newly introduced synthetic paired dataset and subsequently fine-tuned on unlabeled real-world videos via a proposed self-supervised method. Experiments show our method outperforms state-of-the-art ones. Code and data will be available at https://zhang-shuohao.github.io/DeAltHDR/.

Published as a conference paper at ICLR 2026
DEALTHDR: LEARNING HDR VIDEO RECONSTRUC- TION FROM DEGRADED ALTERNATING EXPOSURE SE- QUENCES
Shuohao Zhang', Zhilu Zhang'; Rongjian Xu², Xiaohe Wu', Wangmeng Zuo
Harbin Institute of Technology, Harbin, China
2 City University of Hong Kong, Hong Kong
twesomezshaoe gma i com, toang com, hwt.:oonn...maaiam com, csxhwulgma 1. com, wmzuo@h hinuhchhiee edu.cn
ABSTRACT
High dynamic range (HDR) video be reconstructed from low dynamic range (LDR) sequences with alternating exposures. However, most existing methods overlook the degradations (e.g., noise and blur) in LDR frames, focusing only the brightness and position differences between them. To address this gap, we propose DeAltHDR, novel framework for high-quality HDR video recon- struction from degraded sequences. Our framework addresses two key chal- lenges. First, noisy and blurry content complicate inter-frame alignment. To tackle this, propose flow-guided masked attention mechanism that leverages optical flow for dynamic sparse cross aatssnattntiion computation, achieving supe- rior performance while maintaining efficiency. Notably, its controllable attention ratio allows for adaptive inference costs. Second, the lack of real-world paired data hinders practical deployment. We overcome this with two-stage training paradigm: the model is first pre-trained on our newly introduced synthetic paired dataset and subsequently fine-tuned on unlabeled real-world videos proposed self- seff-sueeriieed method. Experiments show our method outperforms state-of of- the- art ones. Code and data will be available https: /zanggsshohaas shuohac. github. coctuubboo//n 1o.DeAltHDRR
INTRODUCTION
High dynamic range (HDR) imaging (Fairchild 2007 Grosch et al. 2006 Yan al. 2019), which is renowned for its ability to preserve details across an extensive luminance range (from deep shadows to bright highlights), offers more immersive and realistic visual experience. This has catalyzed substantial demand for HDR content across diverse domains, including film production and mobile photography. While specialized hardware (Tocci al.] 2011] Kronander et al. 2013)] Choi et al., 2017 Nayar & Mitsunaga 2000) can capture multiple exposures simultancously to generate HDR assets, these systems are often hindered by high costs and limited portability. Consequently, com- putational methods that reconstruct HDR content from asynchronous multi-exposure low dynamic range (LDR) sequences have emerged as more practical and cost-effective alternative. promi-
nent task is HDR video reconstruction (Kang et al. 2003 Kalantari et al. 2013: Chen et al. 2021
from LDR frames captured with alternating short and long exposures.
However, a critical limitation of most existing HDR video reconstruction methods (Chen et al. 2021 2023b
Chung & 202330 Cui et al. (024) is their underlying assumption of noise-free and blur- in- put frames. Consequently, their designs focus primarily compensating for inter-frame brightness variations and spatial misalignment to mitigate ghosting artifacts, while overlooking crucial degra- dations. This idealized assumption rarely holds true practice. The alternating exposure strategy can inherently introduce artifacts: short-exposure frames are often corrupted by significant noise, particularly in low-light conditions, while long-exposure frames susceptible motion blur from
"Corresponding Author.

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Published as a conference paper at ICLR 2026
camera shake object movement. This gap between assumption and reality severely hinders the applicabilitty of existing methods in real-world scenarios.
Although recent work, BracketRRE (Zhang et al. 2025), does consider such degradations, it was specifically engineered for HDR image reconstruction. As a result, it yields suboptimal performance when directly applied to videos. In this work, propose novel framework to reconstruct high- quality HDR videos from degraded alternating exposure frames, named DeAlHDDR The framework addresses two key challenges. Firstly, inter-frame alignment a critical but non-trivial issue due to complex object motions and occlusions. Moreover, the noise and blur degradations further increase the difficulty of alignment. In this case, the commonly used optical flow (Xu et al.] 2024 Cui al., 2024 Kong et al., (2024) and deformable convolution (Chan et al.) 2022) alignment exhibit limited performance. Attention-based methods (Chung Cho 2023a Tel et al. 2023a) can be effective, but their high computational complexity and large time demands present substantial burden. To address these issues, we propose a novel Flow-Guided Masked Attention (FGMA) alignment mecha- nism, which integrates optical flow and attention methods flexibly and elegantly. Specifically, it first calculates a binary mask to identify 'unreliable' regions where the flow-based alignment is likely cross-attentie operation is applied only within these masked
to be inaccurate. Subsequently, gions, while the rest rely on the efficient flow-based warping. This sparse and targeted application of attention achieves superior balance between performance and computational cost. Crucially, the attention ratio can be dynamically adjustable during inference, enabling the model's computational footprint to be tailored diverse computational budgets, as shown in Fig. !
Secondly, the scarcity of paired real-worid training data presents a critical bottleneck for practical deployment. Models trained solely on synthetic data inevitably suffer from significant performance degradation when applied to real- world scenarios. To bridge this gap, we adopt the two-stage training paradigm from Brack- cURE (Zhang et al.) 2025): pre-training the model synthetic paired data, followed by self-supervised fine-tuning on unlabeled real- world data. To facilitate this strategy, we pro- pose two new datasets. For the pre-training stage, we construct synthetic dataset by ap- plying noise and motion blur to high-quality 4K HDR videos captured with a DJI Pocket 3. For the fine-tuning stage, we collect al- ternating exposure sequences with real degra- dations using an iPhone 16 ProMax. Never- theless, we observe that directly applying the self-supervised fine-tuning method from Brack- eURE (originally designed for HDR image re- construction) is insufficient for the videos. It struggles to adapt to the diverse types and mag- nitudes of motion present in HDR video, yielding only marginal performance gains. We therefore propose a novel motion-enhanced self-suuee adaptation method, specifically engineered to han- dle complex temporal dynamics. Comprehensive experiments on both our synthetic and real-world datasets validate that our proposed method significantly outperforms existing state-of-the-art ones. Our contributions can be briefly summarized as follows:
32.70
32.05
32.60
DeAltHDR
32.35
PSNR(dB)
32:50
32.45
32.30
HDRFlow
32.25
32.20
100
130
120
130
140
150
160
170
FLOPs(G)
Figure 1: Comparison with HDRFlow (Xu et al,, 2024). which is representative sfate-of-the-art method that balances performance and efficiency. Our DeAltHDR outperforms while the inference cost can be adjusted.
We take the noise and blur degradations in alternating exposure frames into account, proposing novel framework to reconstruct nigh-quality HDR videos from them.
We propose a flow-guiie now-guided masked attention for efficient inter-frame alignment, where the attention ratio can be dynamically adjustable for adaptive inference cost.
We introduce motion-enhanced self-supervised fine-tuning approach to improve the re- construction quality on real-world videos.
We construct synthetic and real-world datasets with rich scenes. Experiments on them show our method outperforms the state-of of offthhe-rt ones.
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2 RELATED WORK
2.1 HDR IMAGE RECONSTRUCTION
HDR image reconstruction aims to render one HDR image from multiple LDR images with dif- ferent exposures. Optical flow methods Yuue et al., 2023 Zimmer et al.) 201T) and patch-based methods (Hu et al., 2013; Sen et al.) 2012) are methods which have been proposed for aligning LDR images. However, they fail to reconstruct ghost-free HDR images with motions. With the advancement of deep learning, numerous methods (Wu et al.) 2018 Liu et al. [202] Yan et al,
2019 Zhang et al.) have been proposed for HDR image reconstruction. AHDRNet (Yan et al,
2019 uses spatial attention to guide the HDR image reconstruction to avoid the artifacts generated by optical-flow estimation error. SCTNet (Tel et al. (023a) proposes a network with spatial and channel attentions, which aim to address the intra-image correlation for dynamic motion and the inter-image intertwining for semantic consistency across frames, respectively. SAFNet (Kong et al., 2024) focuses on finding valuable regions while estimating their easily detectable and meaningful motion for efficiency. It also devises a new window partition cropping method for training to fa- cilitate learning on samples with large motion. Although these methods can achieve good results on HDR image reconstruction with clean LDR images, they overlook degradations in LDR images, which are common in real-world scenarios. BracketIRE (Zhang al.] (025) takes noise and blur into account and reconstructs HDR images from degraded LDR images.
2.2
HDR VIDEO RECONSTRUCTION
HDR videos can be captured by specialized hardware, including scan-line or pixel exposure (Choi
et al. 2017 Nayar & Mitsunaga, 2000), beam splitter TTcci al. 2011; I; Kronander et al. 2013
McGuire et al. 2007), modulo or gradient camera (Zhao et al. 2015 Tumblin et al. (005). How- often hindered by high costs and limited portability. Therefore, computa- tional methods that reconstruct HDR content from asynchronous multi-exposure LDR sequences
ever, these systems
have emerged as a more practical and cost-effective alternative. Kalantari et al. (Kalantari et al.) 2013) utilizes optical flow and patch-based optimization algorithm to synthesize missing exposures for each frame. Chen et al. (Chen et al.) (02TT) introduces a coarse-to-fine deep learning framework for HDR video reconstruction consisting of coarse alignment by optical flow and more sophisticated alignment by deformable convolution. LAN-HDE (Chung Cho, (023a) proposes a luminance- based alignment network consisting of an alignment module and hallucination module. Instead of optical flow, it utilizes sparse attention to align frames by evaluating luminance and color in- formation. NECHDR (Cui et al. (224)) proposes framework for HDR video reconstruction by reconstructing the LDR frames of absent exposures from interpolating neighbor LDR frames in the time dimension. HDRFlow (Xu et al.) 2024) proposes an efficient flow estimator for real-time HDR video reconstruction which employs an HDR domain alignment loss for accurate alignment in satu- rated and dark regions. However, these methods mostly overlook noise and blur degradations, which are common in real-world scenarios where images are susceptible to such degradations.
METHODOLOGY
3.1 PROBLEM DEFINITION AND FORMULATION
In the HDR video reconstruction task, the input LDR video generally consists of LDR frames {L captured under different exposures Ae,. We aim to reconstruct high-quality video, con- sisting of HDR frames [H Following previous works (Xu et al. 2024 Shu et al. 2024). we configure the input frame sequence to the network with three-stop exposure difference. Specif- ically, in this paper we introduce our algorithm for handling videos captured with alternating ex- posures and the exposure is (EV-2.EV+,,EV22... ). Let (L1, Lg,... L2mm11 }} be short-exposure frames and (L,, L4,..., L2m } be long-exposure frames, where m E (1, N/2) and N is an even number. Then, following previous methods focusing on multi-exposure HDR reconstruction, we normalize input luminance to ensure consistency. Firstly, we apply a simple inverse gamma correction to obtain linearized input RGB images {L. Then we normalize all ong-exxpsure
frames (L2 1 Aez//
Therefore, adjust the brightness of all long exposures match N/2 ||
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Deceder
Marik Micippocoo
Fram
Oycccll
Warp
Muliplication
Decodder
Time
Long Eagp-Epoounc
Frunc Enceda
Micdll
F
UF F.1)
(F Final
Maskc Calculation
Slant-Expouut Fra::
(B Finl
Query Projection
Key Projectio
Vala: Projaciio
Doh Franz Lest
Festares fune
(a) Overview of procesing the 1-th frame
(b) Flom-Guidd Crid-Guieeddeeee Masked Attention A Alignment (TGMA)
Figure 2: Overview our framework. Figure (a) illustrates the processing of the t-th frame in DeAltHDR, where DeAltHDR uses the other neighboring frames for assistance. Taking the alignment from /. 1-th frame to 1-th frame as an example, figure (b) shows how Plow-Guided Mask Attention Alignment (FGMA) works.
the short exposures. For expot-exposure frames, we keep them unchanged. We define the sequence formed by these new linear frames {Σ,) The input of the network is the concatenation of (1)) and its gamma-transformed images,ie..
[ = {L, (
(1)
where y represents the gamma correction parameter and is generally 1/2.2. Finally, we feed these concatenated images into model B with parameters Og, i.e.,
N {A.) = B(LL)) OB),
(2)
where { is the generated video sequences. The key to HDR video reconstruction lies in constructing model and optimizing its parameters Og.
3.2 OVERVIEW OF NETWORK DESIGN
Our proposed DeAlHDD framework is based multi-scale encoder-decoder architecture, as shown Fig. a). We build based on Turtle (haseemaadii et al. (002)), and replace its alignment module in the decoder block with proposed flow-guided masked attention alignment module while keeping its frame history router as the fusion block. Moreover, we deploy two encoders with identical architecture for short-exposure and long-exposure frames, respectively. This will be helpful for feature extraction in specific exposure domain.
Given an LDR frame LÇ, is first fed to its corresponding encoder to extract multi scali-scale features [F)),1,,233 where indicates the scale of the encoder. In the i-th scale decoder, our proposed flow- guided masked attention alignment module takes the feature from the neighboring frame as well as the feature of the current frame as input. For the t-th frame, let us denote its current feature and the feature of the neighboring frame as F and Fü- respectively. Our module takes Fin and F: as input to calculate the aligned neighboring features Fout i.e.
(3) When implemented, we utilize the FGMA module to calculate and concatenate aligned features of neighboring frames to assist in the reconstruction of the current frame's HDR information. Finally, in the fusion router block, dynamic routing mechanism adaptively weights and combines motion- compensated neighboring features according to their relevance for current frame restoration.
Fout Fr- = FF,.
3.3 FLOW-GUIDED MASKED ATTENTION ALIGNMENT
Inter- fiter-frame alignment plays a crucial role in video restoration. achieve this goal, previous align- ment approaches fall mainly into two categories: optical flow-hased methods and attention-based
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implicit alignment techniques. However, when dealing with LDR frames with noise and blur degra- dations, optical flow and deformable convolution aligument exhibit limited performance. Attention- based methods achieve better quality, but with significantly higher computational costs. We also note that MIA-VSR (Zhou et al. 2024) proposed sparse attention in video super-resolution based on mask calculated from the difference between adjacent frames. However, this approach is less suitable for HDR video reconstruction due to the significant difference in exposure and degradation between neighboring LDR frames. In addition, the computational cost of these methods is fixed and cannot be adaptively adjusted to varying computational budgets. In this paper, we propose novel Flow-Guided Masked Attention (FGMA) alignment mechanism to address the above issues.
Below, we present details of how we generate the aligned neighboring features by taking the align- ment from / 1-th frame to I-th frame as an example. As shown in Fig. [b), firstly, we adopt SpyNet, pretrained light-weight optical flow network, to calculate the bidirectional flow O,-1-e and ( Secondly, We use the forward-backward consistency check to detect 'unreliable' re- gions where the flow-based alignment is likely to be inaccurate, denoted as mask M Specif- ically, we first warp Ly to Li-1 and obtain L and then warp L back to L, and obtain L
- = Warp (L,, O1-1-1), 1 Warp (Im-t-1; Oc-1-1).
(4)
The absolute difference between L, and 1--t quantify the inconsistency in bidirectional flow warping and serves as a direct measure of occlusion regions, i.e.,
Di-1- (( (i,j))
(5)
where D denotes the absolute difference map. Then, we introduce sensitivity factor s to compute the occlusion mask, i.e.,
M1-1-6 (i.))= (1 if D.- (1,1) /255 0.5,
(6)
otherwise.
With this mask obtained, we can identify the occluded regions where optical flow estimation may be unreliable. For these regions, we suggest employing attention mechanisms for alignment refine- ment. Therefore, we compute the query vector with this mask, and keys as well as values from the neighboring frame features, i.e.,
Q = (F O Mc-1-1))
K; = Proja (F
Vi-1-1 = (F
where Projy. Proje. and Proj. represent the point-wise convolution used for linear projections. Finally, the occlusion regions refined by attention concatenated with the warped feature map F flow
t-1-+ and the occlusion mask M, as the output of FGMA module. i.e.
Q QQ-1-kk-1-
For 1- = Softmax
V Vi-1-st:
Vd
F Ffow
Warp (Fi-1, 11-1,0101 O (F (Ffiam F
(8)
Pout
1-1-1 Concal
1- M, 11 paft -
To support dynamic adjustments in testing computational costs, we implemented three alignment branches during training: pure optical flow, pure attention, and our FGMA method. The percentage of non-zeros in the FGMA mask is dynamically controlled by the parameter in Eq. (6). enabling a continuous shift from optical flow attention dominance. Therefore, we set four key boundaries: and s = 0 (optical flow only), = 15 (balancing flow and attention), s = 100 ceteentoo-dominatte s оo (attention-only) along with other 12 sample points: six uniformly sampled from (0, 1] and six uniformly sampled from s E (1, 100). In this way, our experimental results form charac- teristic performance curve as shown Fig. [ For DeAltDDR, the lower left point represents testing only with the optical flow branch, yielding suboptimal performance but at the lowest computational cost. The upper right point represents evaluation with the attention-only branch, which achieves the highest PSNR but at maximal computational complexity. The intermediate points along the curve represent evaluations using our FGMA branch with dynamically adjusted masks, and each point employs different adjusted mask.
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3.4 SELE-SUPERVISED ADAPTATION METHOD
It is still challenging to simulate realistic video sequences in alternating exposure patterns with real-world degradations such as different noise variations and motion blur. The inevitable do- main gap between synthetic datasets and real-world LDR sequences limits the generalization capability of models trained only with synthetic data. As a result, they often produce ar- tifacts during HDR video reconstruction. BrackedRE (Zhang al. 2025) suggests self- supervised fine-tuning method for real-world unlabeled HDR image reconstruction. How- ever, yields suboptimal performance when applied video sequences with diverse mo- tion. To this end, we propose to extend this with a motion-enhanced sampling strategy.
Specifically, the input of the model a sequence of consecutive frames {L;) where L; is the current frame. The output of these 5 frames H.. Then, we cre- ate 3-frame subset for super- vision by: (1) always including the current frame L as the target, (2) randomly selecting one long- exposure neighboring frame and (3) randomly selecting one short- exposure neighboring frame. The output of these 3 frames is de- noted as H, Generally, He per- forms better than H. There- fore, although no ground-truth provided, H, can be seen as the pseudo-target of H, This random sampling strategy introduces inter- frame motion diversity, improving temporal consistency across constructed frames. Moreover, we follow BrackeURE use an ex- ponential moving average (EMA) regulation loss, which can stabi- lize the training process. The de- tailed loss is summarized in Algo- rithm
Algorithm Self- Self-Supervised Adaptation Loss
Require: 5 neighboring frames W + (L
EMA parameters: 3 = 1.0, 0.999
Tone-mapping function T(-) defined as:
T(H)) Tng(1+;*]
lo where μ 5000
Ensure: Self-supervised adaptation loss Ltstal
1: (Frame selection rules}
2: GA + (= E [1,2,3)
3: GB ( (- 1o (1,
4: (Processing inputs}
5: H, B(W; Os)
6: (Dynamic subset construction]
7: L RandomSelectOne(( (A))
8: L + RandomSelectOnel GB)
9: S LA, L
10: [Loss computation)
11: H. + B(S; Qg)
12: Lime [IT(H, (sgg(H))) (Temporal loss)
13: B₁ +- a0 +11-a)0m,
(11-a)
14: H.ma < B(W; Ogm
15: Lema T [T(H,, -ssg((Em))))) (EMA loss}
Liotal Lime + BLmm (Total loss]
17: return Liotal
DATASETS
4.1 SYNTHETIC DATASET
To synthesize more realistic LDR alter-exposure video sequences, need to minimize the gap between synthetic and real frames. First, we capture high-quality HDR 4K/60fps video with a DJI Pocket 3 gimbal camera, ensuring frame-by-frame clarity during high-motion scenarios by its 3-axis mechanical stabilization system. Furthermore, its 1-inch large sensor significantly enhances low- light performance and captures richer details. Next. following the approach of (Nah et al., 2019) to perform frame interpolation. We adopt RIFE (Huang et al. 2022), a pre-trained video frame interpolation model, increase the frame rate by 16 times and get the HDR sequence {H,)² Finally, we introduce our degradation method to simulate alter-exposure sequences. Step 1: the input sequence is divided into non-overlapping 64-frame chunks. Step 2: each group, the first frame is used to simulate the short-exposure frame, while the remaining 63 frames used to simulate the long-exposure frame. Step 3: the HDR images are then converted to LDR by value- clipping and 10-bit linear quantization. Step 4: add heteroscedastic Gaussian noise (Brooks) (et al., 2019: WWan et al. 20200 Hasinoff et al., (010) to the raw version the sequence (which is generated using UPI (Brooks al. 2019)), and adjust the brightness to match the exposure values

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Table 1: Quantitative comparison with state-of-the-art HDR restoration methods both synthetic and real-world datasets. The best results are bolded, and the second-bes besesttetttete results are underlined.
Synthetic LPIPS]
World
Methods
PSNR1
SSIM
HDR-VDP-21
CLIPIQAT
MANIQA1
AHDRNet (CVPR 19)
31.57
0,9588
0.226
68.24
0.2032
0.2098
SCTNet (ICCV 23)
31.95
0.9618
0.205
73.34
0,2320
0,2492
HDR Image
SAFNet (ECCV 24)
32.02
0.9619
0.202
72.96
0.2423
0,2502
BracketRRE (ICLR 25)
32.17
0.9623
0.200
75.32
0.2584
0.2692
Chen (ICCV 21)
31.98
0.9612
0.208
75.67
0.2356
0.2472
HDR Video
LAN-HDR (ICCV 23)
32.04
0,9614
0.211
76,02
0.2546
0.2634
NECHDR (ACM MM 24)
32.16
0.9619
0,205
75.42
0.2578
0.2706
HDRFlow (CVPR 24)
32.26
01,6629
0.196
76.56
0.2601
0,2694
w/o Aduptation (s
15)
32.55
0.9644
0.192
77.02
0.2621
0.2734
DeAlHDR
w/ Adaptation
0.2679
defined in Section 3.1 The noise variance depends on the intensity of the pixels, with parameters
estimated from captured real-world images. In total, we collected 200 scenes (100 daytime scenes / 100 nighttime scenes), including 4000 data pairs (200 data pairs in each scene). 176 scenes (88 daytime scenes / 88 nighttime scenes) are used for training. while the remaining 24 scenes (12 daytime scenes / 12 nighttime scenes) are used for testing. More details on the comparison with other datasets are provided in Appendix
4.2 REAL-WORLD DATASET
To construct realistic HDR video dataset with real noise and motion blur, we used an iPhone 16 Pro Max with the ProShot App. This App is configured to capture alternating-exposure pairs (EV-2 and EV+1) in both RAW (DNG) and RGB format, with a fixed ISO. worth noticing that we intentionally shake the iPhone slightly preserve natural motion blur. Moreover, global motion (only the camera moving). local motion (only the foreground is moving), and full motion (both the camera and foreground moving) scenes are collected in both daytime and nighttime. Finally, we collected 100 alter-exposed video sequences, each of them contains 100 frames. 80 scenes are used for training, and the remaining 20 scenes used for testing. More details of comparison with other datasets is written in Appendix
5
EXPERIMENTS
5.1 EXPERIMENT SETUP
Network Details Following the settings of Turtle (Ghasemabadi et al. 2024). we adopt a 5-frame input structure with specialized dual-encoder processing. We employ & loss and VGG perceptual loss to optimize the network, which can be defined as,
L1 [T(( - T(H;") 1|,,
Logg = (lo.(((())) (.(
where A, is the t-th frame and H the t-th ground truth frame. de(-) denotes the feature extractor from the i-th layer of VGG16 network. Specifically, the total loss be defined as
Liotal Li Nugg
(10)
Implementation details To enable evaluation with dynamically adjustable computational costs, we implement a hybrid alignment strategy. Specifically, 30% the training batches utilize optical flow-based alignment, while another 30% employ implicit attention mechanisms. The remaining 40% batches use our proposed Flow-Guided Mask Attention Alignment, where the mask size is randomly determined by adjusting the threshold parameter in Eq. (6). We implemented our network PoTor (Paszke et al. 2019) and conducted experiments on a single NVIDIA RTX A6000 (48GB) GPU. The batch size set to 8 and the input patch size is set 192 x 192. We adopt

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R
FIBS
060D0
I H
NECHIRR
1IDRTive
Oam
SOYE
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Figure 3: Comparisons
synthetic dataset.
Figure 4: Comparisons on real- dataset.
Table 2: Temporal consistency evaluation on the synthetic dataset.
Method
PSNR
TWE.
TLP4
TOF
HDRFlow
32.26
2.84
2.8756
4.0238
NECHDR
32.16
2.91
3.0486
4.3642
DeAltDR
32.55
2.68
2.7142
3,2117
Table 3: Comparison computational costs.
Table 4: Effect of alignment methods.
Methods
FLOPS (G)
Time (ms)
FLOPs
Time
Alignment Methods
PSNR1
AHDRNet (CVPR 19)
146
140
SCTNet (ICCV 23)
338
356
Flow Guided Defor. Conv.
32.42
SAFNet (ECCV
268
290
Guided Defor. Attention
32,46
202
244
BrackeURE (ICLR 25)
382
387
Patch Aligmment
32.41
178
198
Chen et al. (ICCV 21)
282
302
DeAItHDR (s 0)
32.42
84
LAN-HDR (ICCV 23)
332
325
DeAlHDDR 0.71)
32.49
NECHDR (ACM MM
296
320
DeAItHDR (8 15)
32.55
128
152
HDRFlow (CVPR 24)
116
128
DeAltHDR (s 100)
32.63
159
198
DeAltHDR (* 15)
128
152
DeAltHDR oo)
32.65
the AdamW optimizer (Loshchilov & Hutter 2017 with 81 0.9 and 33 0.999. The models are trained for 250 epochs on the synthetic dataset and 20 epochs on the real-world dataset, with an initial learning rate of 4e and 1e respectively. We use the cosine annealing strategy (Loshchilov & Hutter 2016) to decrease the learning rates to le le_T. Angg is 0.5.
5.2 EVALUATIONS AND COMPARISON CONFIGURATION
Evaluation Configurations For the synthetic dataset, we adopt PSNR, SSIM (Wang et al. 2004). VDP-2 HDR-VDP-2 (Mantiuk al. 2011) evaluation metrics. The PSNR. SSIM and LPIPS are computed in the fal tonemapped domain. For the real-world ones, no-reference metries CLIPIQA (Wang al. 2023) and MANIQA (Yang al., 2022 as evaluation metrics in the absence of ground-truth HDR images. Moreover, we conducted additional experiments evaluate temporal consistency with three widely-used metrics in video processing: 1OF (Chu et al. (020), which measures the pixel-wise difference in motion es-timated from se- quences; ILP (Chu al., 2020), which assesses perceptual changes over time using deep feature maps; and Temporal Warping Error(TWEE), which quantifies frame-to-frand consistency following motion compensation.
LPIPS (Zhang et al. 2018) and HDR-
Comparison Configurations We compare our method with of-th--rtt approaches, including HDR image restoration methods (i.e. AHDRNet (Yan et al. 2019). SCTNet (Tel et al., 2023b), SAFNet (Kong et al. 2024). and BracketIRE (Zhang et al. (2025) and HDR video restoration meth- 2021). LAN-HDR (Chung & Cho 2023a), NECHDR (Cui et al.) ods (i.e. Chen et al. CChen et al.
)024 For evaluation synthetic images, use our synthetic dataset to train all these methods. For evaluation on real-world images, we use the original pre- trained models released by these methods. while our models are further adapted to real-world data by the proposed self-supervised adaptation method. Furthermore, we also conducted experiments on other datasets in Appendix G
2024), and HDRFlow (Xu et al.
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Table 5: Ablation study on encoder parameter sharing strategies. "V" indicates parameters are independent between long and short exposures, while "X" indicates shared parameters. Best results are bold.
Level
Level 2
Level
PSNRT
SSIMT
LPIPS
HDR-VDP-2T
32.55
0.9644
0.192
77.02
32.40
0.9623
0.195
76.52
X
32.18
0.9598
0.204
75.56
X
31.96
0,9587
0.211
74.78
Table 6: methods.
Effect of self-supervised adaptation
Sule-Supe Methods
CLIPIQA
MANIQA1
Adaptation
0,2621
0.2734
TMRNct 0.2648
0.2732
TMKNiit
Oare
Ours
0.2679
0.2774
Figure Effect of adaptation methods.
5.3 EXPERIMENT RESULTS
Results on Synthetic Dataset As shown in Table the middle four columns report full-reference evaluation results on the synthetic dataset, including PSNR, SSIM, LPIPS, and HDR-VDP-2 Our method outperforms all competing HDR image and video reconstruction methods across these met- rics. These improvements validate the effectiveness of our flow-guided masked attention alignment strategy. As visualized in Fig. 3 our method restores more structure and reduces artifacts more effectively than previous approaches. Moreover, shown in Table 2 our DeAltHDR outperforms both NECHDR (Cui al. 2024) and HDRFlow (Xu al. 2024 across all three metrics. This means that our DeAltHDR not only performs well on single imgess, but also has better temporal consistency and the generated videos are much smoother and have less flickering.
Results on Real-World Dataset The last two columns of Table 1 present no-reference evaluation results on real-world data, using CLIP-IQA and MANIQA. Even trained solely on synthetic data (i.e. w/o Adaptation), DeAItDDR outperforms existing methods when directly evaluated real-world datasets. This demonstrates the strong generalization ability our model design. Furthermore, by applying proposed self- supervied adaptation strategy (i.e. w/ Adaptation). DeAltHDR achieves further improvements. As shown Fig. 4 models trained on synthetic datasets tend to produce visible artifacts when applied to real-world data. By introducing our self-supervised adaptation method, these artifacts are effectively reduced, leading to improved visual quality in real-world scenarios. More results be found in Appendix G
Computational Efficiency As shown in Table 3 DeAltHDR achieves competitive computational efficiency and has comparable inference time HDRFlow, currently the fastest baseline. Com- pared to other state-of-the-art methods, it is significantly more efficient in both computational cost and runtime. In summary, our method not only achieves better reconstruction performance, but also maintains high efficiency. More details can be found in Appendix
5.4 ABLATION STUDY
Effect of Flow-Guided Masked Attention Alignment To validate the effectiveness of our proposed Flow-Guided Masked Attention (FGMA), we replace the alignment module DeAltHDR with sev- eral existing alternatives. Specifically, we adopt guided deformable attention from RVRT (Liang (Shi et al.) 2022) and flow-guided deformable convolution from VSs (Chan et al. 2022). As shown in Table 4 FGMA equipped DeAltHDR not only achieves a higher PSNR but also significantly reduces computational cost, demonstrating the effectiveness of FGMA in balancing reconstruction quality and efficiency.
ct al., 2022), patch alignment from
Effeet of dual-encoder processing We also discuss the role of the dual-encoder design. We con- ducted ablation study with four different training configurations: 1) using independent parameters

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at all three levels (no sharing), 2) sharing parameters across all three levels, 3) sharing parameters only at level 3, and 4) sharing parameters at levels and but not at level 1.
As shown in Table | the best performance is achieved when no parameters are shared across the three levels. This result can be attributed to the inherent characteristics of the imput data: short- exposure frames exhibit higher noise levels, while long-exposure frames suffer from more severe motion blur. Therefore, using shared parameters for the encoders, which process these fundamen- tally different inputs, is suboptimal. As a result, independent encoders are more effective, they specialize in extracting features from their respective inputs with distinct artifacts.
Effect of Sel--Supervised Adaptation To evaluate the effectiveness of self-supervised adapta- tion strategy, we replace the fine-tuning scheme in DeAltHDR with that of TMRNet (Zhang et al.) 2025). TMRNet also adopts a multi-frame self-supervised loss, but its training frames are limited to strict subset of the input sequence. As result, it limits performance in video HDR reconstruction, where large motion ranges are common. Instead, we propose to extend the frame sampling range from t - 2 to / + 2 (5 frames) to ( - 6 to t 6 (13 frames) to capture larger motion ranges. To maintain the dynamic range of sparse frames, we randomly sample one short-exposure frame and one long-exposure frame from this extended range. As shown in Ta our self-supervised adap- tation method achieves better performance in both CLIP-IQA and MANIQA compared to TMRNet and the bascline (i.e. w/o Adaptation). As shown in Fig. our method is able to reconstruct more accurate HDR videos with finer detail preservation and fewer artifacts.
CONCLUSION
In this paper, we propose DeAltHDR, robust framework for high-quality HDR video reconstruction from degraded alternating-exposure sequences. Our approach introduces three key innovations: (1) a Flow-Guided Masked Attention(FGMas mechanism that dynamically combines optical flow and sparse attention for efficient alignment under noise and blur, (2) mnhhneee self-supervised adaptation method for effective real-world fine-tuning, and (3) comprehensive synthetic and real- world datasets that capture authentic noise and motion blur characteristies. Extensive experiments demonstrate that DeAltHDR outperforms state-of-the-art methods in both synthetic and real-world datasets due to our FGMA module and self-supervised method. To the best of our knowledge, DeAltHDR is the first framework that addresses noise, blur, and motion challenges in alternating- exposure HDR video reconstruction.
7 APPLICATIONS AND LIMITATIONS
Applications. A primary application of this work is high-quality HDR video capture in challenging lighting conditions, particularly for dynamic nighttime scenes. Our method enables smartphones and consumer cameras to produce HDR videos that preserve both bright highlights and dark details while remaining free from noise and motion blur artifacts. This capability is valuable for various practical scenarios, including mobile cinematography, live streaming, surveillance systems, and documentary filmmaking in lightight environments. We demonstrate its effectiveness on real-world data captured with commodity devices (i.e., iPhone 16 Pro Max), showing that our approach is readily deployable on consumer hardware. Moreover, the adjustable computational cost feature allows users to balance quality and speed based on their specific needs, making it suitable for both real-time applications and offline processing.
Limitations. Despite its effectiveness, our method has several limitations. First, it requires dnternatinn-expostiie input sequences, which limits its applicability to videos captured with stan- dard fixed-exposure settings. Second, may fail in extreme cases where all input frames lack sharp references due to severe camera shake or fast motion, as the multi-frame fusion mechanism relies on complementary sharp information. Third, given the diverse noise characteristics of different camera sensors, our model trained one sensor may exhibit limited generalization when applied to sensors with significantly different noise profiles. Finally, although our method achieves competitive effi- ciency, the computational cost remains non-trivial for real-time processing on resource-conster constrraid devices, particularly when using higher attention ratios. We leave the investigation a more gen- eral, sensor-agnostic model for future work.
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 62476067, and the China Postdoctoral Science Foundation under Grant No. 2025M784371.
REFERENCES
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, and Jonathan Barron. Unprocessing images for learned raw denoising. In Proceedings of the IEEE/CVF conference computer vision and pattrrn recognition, pp. 11036-11086 2019.
I BT. Methodologies for the subjective assessment of the quality of television images, document recommendation itu-r bt. 500-14 4 (10/2019). ITU, Geneva, Switzerland, 2020.
Kelvin CK Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basievsr++: Improv- ing video super-resolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5972-5981, 2022.
Guanying Chen, Chaofeng Chen, Shi Guo, Zhetong Liang, Kwan- K Wong, and Lei Zhang. Hdr video reconstruction: A coarse-to-fine network and real-world benchmark dataset. Proceed- ings of the IEEE/CVF international conference computer vision, pp. 2502-2511, 2021.
Inchang Choi, Seung-Hwan Back, and Min Kim. Reconstructing interlaced nigh-dynamic-range video using joint learning. IEEE Transactions on Image Processing, 26(1 1):5353-55333 5366, 2017.
Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixe and Nils Thuerey. Learning temporal coher- ence via self-supervision for gan-based video generation. ACM Transactions on Graphics (TOG), 39(4))75-1, 2020.
Haesoo Chung and Nam Cho. Lan-hdr: Luminance-basee alignment network for high dynamic range video reconstruction. In Proceedings of the IEEE/CVF International Conference Com- puter Vision, pp. 12760-12769, 2023a.
Haesoo Chung and Nam Ik Cho. Lan-hdr: Luminance-basee alignment network for high dynamic range video reconstruction. In Proceedings of the IEEE/CVF International Conference on Com- puter Vision, pp. 1 2023b.
Jiahao Cui, Wei Jiang, Zhan Peng, Zhiyu Pan, and Zhiguo Cao. Exposure completing for tempo- rally consistent neural high dynamic range video rendering. In Proceedings of the 32nd ACM International Conference Multimedia, pp. 10027-10035, 2024.
Mark Fairchild. The hdr photographic survey. In Color and imaging conference, volume 15, pp- 233-238. Society of Imaging Science and Technology, 2007.
Jan Frochlich, Stefan Grandinetti, Bernd Eberhardt, Simon Walter, Andreas Schilling, and Harald Brendel. Creating cinematic wide gamut hdr-video for the evaluation of tone mapping operators and hdr-displays. In Digital photography X. volume 9023, pp. 279-288, SPIE, 2014.
Amirhosein Ghasemabadi, Muhammad Janjua, Mohammad Salameh, and Di Niu. Learning trun- cated causal history model for video restoration. Advances in Neural Information Processing Systems, 37:27584 2024.
Thorsten Grosch et al. Fast and robust high dynamic range image generation with camera and object movement. Vision, Modeling and Visualization, RWTH Aachen, pp. 277-284, 2006.
Cheng Guo, Leidong Fan, Ziyu Xue, and Xiuhua Jiang. Learning a practical sdr-to-hdrty up- conversion using new dataset and degradation models. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pp. 22231-22241, 2023.
Samuel W Hasinoff, Frédo Durand, and William T Freeman. Noise-optimal capture for high dy- namic range photography. In 2010 IEEE Computer Society Conference on Computer Vision and Patlern Recognition, pp. 553-560. IEEE, 2010.

---

Published as a conference paper at ICLR 2026
Jun Hu, Orazio Gallo, Kari Pulli, and Xiaobai Sun. Hdr deghosting: How to deal with saturation? In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1163-1170, 2013.
Xiangyu Hu, Liquan Shen, Mingxing Jiang, Ran Ma, and Ping An. La-hdr: Light adaptive hdr reconstruction framework for single Idr image considering varied light conditions. IEEE Trans- actions on Multimedia, 25:4814-4429, 2022.
Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time intermediate flow estimation for video frame interpolation. In European Conference on Computer Vision, pp. 624 642. Springer, 2022.
Siddhant Jain, Daniel Watson, Eric Tabellion, Ben Poole, Janne Kontkanen, et al. Video interpola- tion with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7341-7351 2024.
Nima Khademi Kalantari, Eli Shechtman, Connelly Barnes, Soheil Darabi, Dan Goldman, and Pradeep Sen. Patch-based high dynamic range video. ACM Trans. Graph., 32(6):202-1, 2013.
Sing Bing Kang, Matthew Uyttendaele, Simon Winder, and Richard Szeliski. High dynamic range video. ACM Transactions On Graphics (TOG), 22(3):319-322 2003.
Lingtong Kong, Bo Li, Yike Xiong, Hao Zhang, Hong Gu, and Jinwei Chen. Safnet: Selective alignment fusion network for efficient hdr imaging. In European Conference Computer Vision, pp. 256-273. Springer, 2024.
Joel Kronander, Stefan Gustavson, Gerhard Bonnet, Anders Ynnerman, and Jonas Unger. A unified framework for multi-sensor hdr video reconstruction, 2013. URL https::/arrii..okt
abs abs/1308 0////138.4499 4908
Jingyun Liang. Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, and Luc V Gool. Recurrent video restoration transformer with guided deformable attention. Advances in Neural Information Processing Systems, 35:378-393, 2022.
Shuaizheng Liu, Xindong Zhang, Lingchen Sun, Zhetong Liang. Hui Zeng, and Lei Zhang. Joint hdr denoising and fusion: A real-world mobile hdr image dataset. In Proceedings of the IEEF/CVF Conference Computer Vision and Pattern Recognition, pp. (3966-13977 2023.
Xinhao Liu, Masayuki Tanaka, and Masatoshi Okutomi. Noise level estimation using weak tex- tured patches a single noisy image. In 2012 19th IEEE International Conference on Image Processing, pp. 665-668. IEEE, 2012.
Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint 1608: 03983, 2016.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv: 2017.
Rafat Mantiuk, Kil Joong Kim, Allan G Rempel, and Wolfgang Heidrich. Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Transactions on graphics (TOG), 30(4):1-14, 2011.
Morgan McGuire, Wojeiech Matusik, Hanspeter Pfister, Billy Chen, John Hughes, and Shree K Nayar. Optical splitting trees for high-precision monocular imaging. IEEE Computer Graphics and Applications, 27(2):32 42, 2007.
Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge video deblurring and super-resolution: Dataset and study, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019.
Niranjan D Narvekar and Lina J Karam. A no-reference image blur metric based on the cumulative probability of blur detection (cpbd). IEEE Transactions on Image Processing, 20(9):2678-260 2683,, 2011.
12

---

Published as a conference paper at ICLR 2026
Shree K. Nayar and Tomoo Mitsunaga. High dynamic range imaging: Spatially varying pixel ex- posures. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), volume 1, pp. 472-479. IEEE, 2000.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury. Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high- performance deep learning library. Advances in neural information processing systems, 32, 2019.
Pradeep Sen, Nima Khademi Kalantari, Maziar Yaesoubi, Soheil Darabi, Dan B Goldman, and Eli Shechtman. Robust patch-based hdr reconstruction of dynamic scenes. ACM Trans. Graph., (6):203-1,2014 2012.
Shuwei Shi, Jinjin Gu, Liangbin Xie, Xintao Wang, Yujuu Yang, and Chao Dong. Rethinking align- ment in video super-resolution transformers. Advances in Neural Information Processing Systems, 35:36081-3660 2022.
Yong Shu, Liquan Shen, Xiangyu Hu, Mengyao Li, and Zihao Zhou. Towards real-world hdr video reconstruction: A large-scale benchmark dataset and two-stage alignment network. Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2879-2888, 2024.
Steven Tel, Zongwei Wu, Yulun Zhang, Barthélémy Heyrman, Cédric Demonceaux, Radu Timofte, and Dominique Ginhac. Alignment-free hdr deghosting with semantics consistent transformer. arXiv preprint urXiv:2305. 18135, 2023a.
Steven Tel. Zongwei Wu, Yulun Zhang, Barthélémy Heyrman, Cédric Demonceaux, Radu Timofte, and Dominique Ginhac. Alignment-free hdr deghosting with semantics consistent transformer. arXiv preprint arXiv:2305. 181.//8555. 2023b.
Michael Tocci, Chris Kiser, Nora Tocci, and Pradeep Sen. A versatile hdr video production system. ACM Transactions on Graphics (TOG), 30(4):1-10, 2011.
Jack Tumblin, Amit Agrawal, and Ramesh Raskar. Why want a gradient camera. In 2005 IEEE Computer Sociery Conference on Computer Vision and Pattern Recognition (CVPR'0S), volume 1, pp. 103-110.10 IEEE, 2005.
Jianyi Wang, Kelvin CK Chan, and Chen Change Loy. Exploring clip for assessing the look and feel of images. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp. 2555-2563, 2023.
Yuzhi Wang, Haibin Huang, Qin Xu, Jiaming Liu, Yiqun Liu, and Jue Wang. Practical deep raw image denoising on mobile devices. In European Conference on Computer Vision, pp. 1-16. Springer, 2020.
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero Simoncelli. Image quality assessment: from error visibility structural similarity. IEEE transactions on image processing, 13(4):600- 612, 2004.
Shangzhe Wu, Jiarui Xu, Yu- Wing Tai, and Chi-Keung Tang. Deep high dynamic range imaging with large foreground motions. In Proceedings of the European Conference Computer Vision (ECCV), pp. 117-132, 2018.
Gangwei Xu, Yujin Wang, Jinwei Gu, Tianfan Xue, and Xin Yang. Hdrflow: Real-time hdr video reconstruction with large motions. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 14851-24866 2024.
Qingsen Yan, Dong Gong, Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, and Yan- ning Zhang. greentdon-guiedd network for ghost-free high dynamic range imaging. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1751-1760, 2019.
13

---

Published as a conference paper at ICLR 2026
Sidi Yang. Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujuu Yang. Maniqa: Multi- dilliidimensin attention network for no-reference image quality assess- ment. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 1191-1200, 2022.
Huanjing Yue, Yubo Peng, Biting Yu, Xuanwu Yin, Zhenyu Zhou, and Jingyu Yang. Hdr video reconstruction with large dynamic dataset in raw and srgb domains. arXiv preprint arXtv:2304.04773, 2023.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness deep features as a perceptual metric. In Pruceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595, 2018.
Zhilu Zhang, Haoyu Wang, Shuai Liu, Xiaotao Wang, LEI LEI, and Wangmeng Zuo. Self- supervised high dynamic range imaging with multi-exposure images in dynamic scenes. In The Twelfth International Conference on Learning Representations.
Zhilu Zhang, Shuohao Zhang, Renlong Wu, Zifei Yan, and Wangmeng Zuo. Exposure bracketing is all you need for high-quality image. In The Thirteenth International Conference on Learning Representations, 2025.
Hang Zhao, Boxin Shi, Christy Fernandez- Culll, Sai-Kit Yeung, and Ramesh Raskar. Unbounded high dynamic range photography using modulo camera. In 2015 IEEE International Conference on Computational Photography (ICCP), pp. 1-10. IEEE, 2015.
Zhihang Zhong, Gurunandan Krishnan, Xiao Sun, Yu Qiao. Sizhuo Ma. and Jian Wang. Clearer frames, anytime: Resolving velocity ambiguity in video frame interpolation. In European Con- ference on Computer Vision, pp. 346-363. Springer, 2024.
Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, and Shuhang Gu. Video super-resolution transformer with masked inter&intra-fraat attention. arXiv preprint arXiv:2401.06312, 2024.
Henning Zimmer, Andrés Bruhn, and Joachim Weickert. Freehand hdr imaging of moving scenes with simultaneous resolution enhancement. In Computer Graphics Forum, volume 30, pp. 405- 414. Wiley Online Library, 2011.
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APPENDIX
The content of the appendix involves:
Details of our synthetic dataset in Appendix A
Comparison with other datasets in Appendix
Details of our encoder and decoder blocks in Appendix C
Discussion on the model's capability for denoising and deblurring Appendix
Comparison of Computational Costs in Appendix
H
Effect of Self-supervised Adaptation in Appendix
More Visual Reults in Appendix G
A DETAILS OF SYNTHETIC DATASET
Fig. provides the overview of our synthetic data generation pipeline. The original HDR videos in our synthetic dataset were captured with the DJI Pocket 3. First, we used the UPI method (Brooks et al., (019) to convert these RGB videos to RAW space to add noise and convert them back to RGB space. Unlike the default parameters in UPI, our method employs the real camera parameters from the DJI Pocket 3 for both the RGB-to-RAWe and RAW-to-RGB conversions. Second, we employ RIFE (Huang et al., 2022) for frame interpolation. We confirm that RIFE is agnostic to dynamic- range encoding of the imput data and can be employed for HDR video interpolation. First, RIFE is fed HDR frames that are normalized float tensors the [0, 1] range. Since RIFE operates internally on floating-poin data, this linear scaling preserves high dynamic range information and relative lu- minance without the quantization issue. Second, the core objective of RIFE is motion estimation and occlusion-aware blending intermediate-time geometric and largely invariant to monotonic intensity transformations (linear scaling, 7-correction or p-law). Therefore, scaling HDR data to [0, 1] ensures numerical stability without distorting the underlying motion field. Unlike classical trightness-constane ric models, RIFE learns flow and fusion weights end-to-end in a normalized feature space. this way, RIFE is agnostic to dynamic-range encoding of the input data. Visual results can be referred to K While it preserves the data distribution as confirmed by visual inspection, the synthesized motion blur still diverges from real-world blur due to its inherent limitations. To address this gap, we propose a self-supervised real-world adaptation approach in this work. Given recent advances in interpolation models for large motions @Jin et al. 2024 and complex textures (Zhong et al., 2024). we believe that this limitation will be progressively mitigated as these technologies continue advance.
0.5). Motion estimation is fundamentally
methods that rely on rigid photomet-
Figure
The noise in RAW images consists primarily of two components: shot noise and read noise (Brooks al. 2019). The shot noise follows Poisson distribution with a mean corresponding to the actual light intensity in photoelectrons, while the read noise can be modeled as a zero-mean Gaussian random variable with constant variance. These noise sources can be jointly approximated heteroskedastic Gaussian random variable N, which can be defined as:
N No0, Aread X),
(A)
where X represents the clean signal intensity and the parameters Aread and Ashot are determined by the sensor's analog and digital gain settings.
To ensure realistic noise synthesis, iPhone 16 Pro Max camera sensor, extracting Ashor and Aread from the raw image metadata. The ISO of the reference images is set to 100 during daytime and 1600 during nighttime. For these im-
calibrate our model using the noise characteristics of the
ages, measured X shu. 3.23 10-4 -4 night shot Ry 4.52 10-3 and X dayy 2.67 x 10-6. Aread a
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HDR RGB 4K/60 fps videos
(Vere1
(V
Gr+M Qt=
Frame Interpolation
RGB RAW
Sum (Simulate Blur
HDR LDR
HDR RAW Video Clip (V.)
Add Noise
Split (Simulate Mution)
RGB
1
l2
Figure A: Overview of our synthetic data generation pipeline. We utilize HDR video to synthesize multi-exposure images {L, [L,)e Qi marks the initial frame, and M specifies the frame numbers used synthesize onn-exxosure frames. The short-exposure ground truth is its corresponding HDR frame. The long-exposure ground truth is the middle frame used to simulate this frame sequence.
4.04 x 10-5. To generate noise with different intensity levels, we sample these parameters uni- formly across the ISO range 50-400 at daytime and 800-3200 nighttime, leading to the following distributions:
log(A (A
~ Ld ((lo((0.00014)) (0g(00006)),
log( (oggAnne sahtt
night
log( (og(A,ea (Ared
log day (A
~M
(B)
M(14428 log( shott 1.215, 0.28%),
night log( (Xrrdd
log(A (Xsighh Angght) )) ~
shot
(1.692 log( shot + 0.25%),
U(,b)) 6) denotes the uniform distribution over interval [a, b).
Table A: Comparative analysis of key characteristics in HDR video datasets. NLE and CPBD quan- tify the challenging artifacts (noise and blur) the input LDR sequences, whereas DR, SI, and stdl. describe the richness and quality of the HDR ground truth.
Datasets
Noise
Blur
NLE,
CPBD
DR1
SI
stdL1
Cinematic Video
18.4
0.65
2.46
9.02
11.26
DeepHDRVideo
X
6.6
0.79
2.54
8.93
11.35
Real-HDRR
V
13.5
0.40
2.73
9.16
12.13
Our Synthetic Dataset
V
28.5
0.28
2.80
9.13
12.42
Our Real-World Dataset
24.6
0.32
B COMPARISON WITH OTHER DATASETS
In this section, we present comparative analysis between our dataset and existing datasets. Cur- rently, DeepHDRVideo (Chen et al.) 202T) and Cinematic Video (Frochlich al.) 2014) are among the most widely adopted datasets for HDR reconstruction of single images and videos. Recently, (Shu al. 2024) presented a new large-scale real-world dataset named Real-HDRV, which contains various scenes and diverse motion patterns. The following comparisons will focus these three key datasets.
A key distinction is that work explicitly accounts for both noise and blur artifacts introduced dur- ing real-world capture processes. Notably, the DeepHDRVideo dataset only accounts for luminance variations between long- and short-exposure frames, but fails incorporate realistic noise and blur degradations. The Cinematic Video dataset is synthesized through simulation of HDR video. The source HDR videos used for the simulation inherently contain motion blur. Both the synthesized
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Table Scene-wise dynamic range statistics for our synthetic dataset.
Scene Type
Scenes
Avg DR
Min (cd/m²)
Max (cd/m²)
Daytime Indoor
16
2,46
0,08
3.2 x 102
Daytime Outdoor
34
2.82
0.15
6.0 x 10a
Nighttime Indoor
14
2.32
0,02
8.0 x 10a
Nighttime Outdoor
36
3.24
5 x 10 10-3
1.2 x 10a
Overall
100
2.71
5 x 10
6.0 x 103
Table C: Component-wise effect on degradation handling. Short-exposure metrics highlight noise suppression (PSNR/NLEJ), while long-exposure metrics focus blur fidelity (PSNR/CPBDt).
Short Exposure (noise) PSNR
Long Exposure (blur) PSNR1
Variant
NLE,
CPBD1
DeAltHDR (s=15)
32.65
2.92
32.45
0.75
w/o Dual Encoders
32.04
7.27
31.88
0.51
Flow-only aligmment (s=0)
32.48
4.05
32.36
0.64
Attention-only alignment (s=oc)
32.74
2.23
32.56
0.79
w/o VGG perceptual loss
32.46
4.11
32.38
0.60
long- and cxpot-cxposure frames simply inherit this blur, without considering the difference in mo- tion blur between long and short exposures. In real-world scenes, short-exposure images should have less blur than long-exposure images. The recently proposed Real-HDRV dataset still mainly con- siders luminance variations, exhibiting less noise and blur. Our synthetic dataset utilizes 4K/60fps HDR videos captured exclusively with DJI Pocket 3, ensuring per-frame sharpness while simulating realistic overexposure and motion blur effects through multi-frame superposition, and each frame has its corresponding ground truth (GT) image. For real-world data acquisition, we employed an iPhone 16 Pro Max with ProShot's bracketing mode while disabling all noise reduction modules to preserve authentic sensor noise characteristics. At the same time, camera shake and object motion enable the generation of physically accurate motion blur in long-exposure frames. Therefore, our dataset is more comprehensive and physically accurate than existing alternatives by incorporating real-world noise and motion blur.
Table A presents the quantitative statistical results for each dataset. NLE (Liu et al. 2012) mea- sures the intensity of image noise, where a lower value indicates less noise, and vise versa. CPBD (Narvekar & Karam 2011) measures the degree of motion blur in images, where lower value indicates more blur and vise versa. In particular, we evaluate NLE only on the short-exposure images and CPBD only the long-exposure images. DR (Hu et al. 2022) calculated the log10 difference between the highest 2% luminance and the lowest 2% luminance. SI (Spatial Informa- tion) defined in (BT 2020), and stdL (standard deviation luminance) is defined in (Guo al. 2023). As shown in Table A our dataset exhibits significant levels of noise and motion blur in the LDR inputs. Moreover, the inclusion of SI, DR, and stdL metrics provides comprehensive character- ization of spatial complexity. luminance range diversity, and contrast richness in HDR ground truth images. Furthermore, we provide comprehensive dataset statistics and visualizations in Table B and more visualization results can be found in Figure F and Figure G
C DETAILS OF OUR ENCODER AND DECODER BLOCK
Our network adopts a U-net architecture with skip connections, featuring three-level encoder and a three-level decoder. The first two levels of the encoder use identical block designs, differing only in the number of blocks and feature resolution. Each level consists of multiple Reduced Attn blocks with standard feedforward networks (FFW). The ReducedAttn module replaces conventional attention mechanisms with a more efficient design: it first expands channels using x convolutions, then applies depthwise 3 x convolutions for spatial mixing, followed by another x convolution two 1 1 convolutions with GELU activation
to project features back. Each FFW component in between to mix channel information. Level 3 of the encoder differs from the first two levels, featuring deeper architecture with six blocks. While levels 1-2 ReducedAttn, level 3 employs
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Pr
LayenNorm
RedaosdAin
LayerNorn
FFW
(a) Thooder Block level
10 Ralaaddez
LayerNorm
Churne/Ai
Layeroorn
GFFW
Uncoder Block level
Nor
Lagar Negm
GFTW
FHR
FGMA
Fusion
P7 Pt.
FI Fl.
P7
Figure B: Details of our network architecture. (a) denotes the block used in the first and second
levels of the encoder. (b) shows the block used in the third level of the encoder. (c) denotes the middle block. (d) denotes the decoder block used in all three levels. (c) shows the FFW block. (f) shows the ReducedAttn block. (g) shows the GFFW block. Specifically, the number of blocks in each level is 2, 4, and 6, while the number of middle blocks is set to 8.
Table D: Short-exposure evaluation results.
Synthtiic (Short)
Real-World (Short)
Methods
PSNR1
SSIM
LPIPS
DDD-21
CLIPIQA
MANIQAt
AHDRNet (CVPR 19)
31.68
0.9595
0.2247
68.51
0.2055
0.2117
SCTNet (ICCV 23)
32.08
0.9624
0.2039
73.58
0,2341
0,2509
HDR Image
SAFNet (ECCV 24)
32.11
0.9624
0.2010
73.19
0,2445
0,2518
BracketIRE (ICLR 25)
32.29
0.9629
0.1988
75.60
0.2604
0,2710
Chen et al. (ICCV 21)
32.08
0.9618
0.2069
75.93
0,2378
0,2489
LAN-HDR (ICCV 23)
32.15
0.9619
0.2098
76.26
0,2569
0,2653
HDR Video
NECHDR (ACM 24)
32.25
0.9625
0.2040
75.65
0,2599
0,2724
HDRF-lww (CVPR 24)
32.34
0.9634
0.1951
76.78
0,2621
0.2711
Adaptation (s 15)
32.65
0.9650
0.1911
77.29
0.2643
0,2752
DeAlHDD
Adaptation
0,2700
0.2791
standard Channel Attention blocks in all layers, enabling global feature interactions. networks are upgraded to Gated FFW blocks, which introduces gating mechanism paralll depth-wise convolutions and element-wise multiplication before feature projection.
The FFW
The Fusion module starts the middle block by applying channel-wise cross-attention layers. These layers maintain temporal coherence through efficient key-value buffers that cache and retrieve fea- tures from neighboring frames, creating stable foundation for motion-aware processing. All de- coder levels share the same fundamental block structure. our network, we use the FGMA align- ment method to align neighboring features, followed by the Fusion module, which is the same as that in the middle block. The detailed architecture shown in Fig.
D DISCUSSION ON THE MODEL'S CAPABILITY FOR DENOISING AND DEBLURRING
In this section, we provide detailed analysis of our model's capability for denoising and deblurring.
Which Component Handles Noise and Blur? The model inherently handles noise and blur by leveraging complementary information from multiple input frames. In addition, conducted comprehensive component-wise ablation study to investigate which component contributes the most. As shown in Table C dual encoders contribute the most by extracting features from inputs with different degradations.
Why Does the Model Handles Blur Without Explicit Deblurring Design Our model successfully handles motion blur through two synergistic, implicit mechanisms that arise from its core design for video HDR.
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Table E: Long-exposure evaluation results.
Synthetic (Long)
Real- World (Long)
Methods
PSNR1
SSIM
LPIPS!
HDR-VDP-21
CLIPIQA
MANIQA1
AHDRNet (CVPR 19)
31.46
0.9581
0.2273
67.97
0.2009
0.2079
SCTNet (ICCV 23)
31.82
0.9612
0.2061
73.10
0.2299
0.2475
HDR Image
SAFNet (ECCV 24)
31.93
0.9614
0.2030
72.73
0,2401
0,2486
BracketIRE (ICLR 25)
32.05
0.9617
0.2012
75.04
0,2564
0,2674
Chen et al. (ICCV 21)
31.88
0.9606
0.2091
75,41
0,2334
0.2455
LAN-HDR (ICCV 23)
31.93
0.9609
0.2122
75.78
0.25223
0,2615
HDR Video
NECHDR (ACM MM 24)
32.07
0.9613
0.2060
75.19
0,2557
0,2688
HDRFlow (CVPR 24)
32.18
0.9624
0.1969
76.34
0.2581
0.2677
w/o Adaptation (s=15)
32.45
0.9638
0.1929
76.75
0,2599
0.2716
DeARHDD
Adaptation
0.2658
0.2757
Table F: Runtime and performance comparison of flow-based and attention- based alignment meth-
ods. Besides the default model, we develop a compact DeAlHDDRSS variant whose inference time is lower than HDRFlow while maintaining quantitative results.
Method
PSNRT
SSIM
LPIPS}
HDR-VDP-21
FLOPs (G)
Time (ms)
HDRFlow
32.26
0.9629
0.196
76.56
116
128
DeAItHDR (Default)
32.55
0.9644
0.192
77.02
128
152
DeAltHDR-S (compact)
32.42
0.9638
0.194
76.70
102
116
First, the fundamental mechanism is deblurring via multi-frame fusion. In alternating exposure sequences, blur is non-uniform: a blurred region in long-exposure frame often corresponds to sharp region complementary short-exposure frame. Our model leverages frame alignment and fusion to effectively remove blur by aggregating sharp information from across the sequence.
Second, the dual-encoder architecture provides exposure-spectee speciifc feature extraction. The short- exposure encoder learns to extract sharp features while suppressing noise, whereas the long- exposure encoder learns to preserve semantic structure and global context despite blur. This ar- chitecture allows the model to take advantage of the distinct strengths of different types of exposure.
Our method demonstrates significantly stronger generalization than traditional, explicitly trained deblurring models. The key distinction lies in its learning objective: instead of learning the blur kernel distribution, our model learns to identify and fuse complementary information across frames. The primary failure mode occurs when all input frames lack sharp reference, which is quite rare scenario in alternating exposure video.
Why Does Adaptation Improve Sharpness? We attribute the sharpness gains to the mitigation of the domain gap between synthetic and real-world blur by our self-supervised adaptation. Specifi- cally, synthetic blur is generated deterministically using linear motion kernels, whereas real-world blur arises from a complex, stochastic interplay of factors like camera shake, object motion, and rolling shutter. Consequently, models trained exclusively on synthetic data are biased and limited to recognizing and recovering simulated blur patterns. Our self-supervised adaptation addresses this gap by minimizing the inconsistency between two reconstructions with differing temporal support: an information-richt sharper estimate from 5 frames (H₂) and an information- poor, blurrier one from 3 frames (H,). During adaptation, the model is trained to align the 3-frame reconstruction with the quality of the 5-frame version, thereby learning to recover sharper details from more degraded inputs. Furthermore, we employ motion-augmented sampling (using frames in 6) to avoid over- fitting synthetic motion patterns. This enhances the model's ability to handle real-world video with a diverse and realistic range of motion blur.
Why Ateee Methods can Restore Blur Although aptical-flow-basee methods can align blurry frames, their performance is limited under severe or complex motion blur because they rely primarily on local information to estimate dense motion fields. Consequently, they accurate for smooth motions but are prone to failures when confronted with significant blur and occlusions. In contrast, attention-based methods leverage global information more fully, which allows them to
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Table G: Runtime and FLOP breakdown of DeAlHDRR 2556x256 patch on RTX A6000).
Component
Time (ms)
Percentage
FLOPs (G)
Input Processing
8
5.3%
2.1
Dual Encoders
42
27.6%
38.4
Short-cxp encoder
21
13.8%
19.2
Long-exp encoder
21
13.8%
19.2
FGMA Alignment
58
38,2%
52.8
Flow estimation (SpyNet)
18
11.8%
8.4
Mask computation
2.6%
Attention (47% pixels)
36
23.7%
42.2
Frame History Router
28
18.4%
24.5
Decoder
12
7.9%
9.8
Output Processing
4
2.6%
0.4
Total
152
100%
128
Table H: Quantitative comparison of different HDR methods on DeepHDRVides (Chen et al. 2021
Dataset, Real-HDRV Shu et al. (024) Dataset and Cinematic Video Freehlic et al. (2014 Dataset.
Dataset
Methods
PSNRT
SSIM
LPIPS
HDR-VDP-21
Chen al.
42.48
0.9620
0.184
74.80
LAN-HDR
41.59
0.9472
0.181
71.34
DeepHDRVideo
NECHDR
43.44
0.9558
0.176
79.20
HDRFlow
43.25
0.9520
0.174
77.29
DeAltDDR
43.78
0.9572
0.172
79.32
Chen al.
36.50
0.9262
0.192
67.56
LAN-HDR
38.27
0.9334
0.184
69.24
Real-HDRV
NECHDR
39.23
0.9428
0.180
72.50
HDRFlow
38.98
0.9434
0.179
72.32
DeAltHDR
40.04
0.9489
0.175
73.14
Chen et al.
35.65
0.8949
0.172
72.09
LAN-HDR
38.22
0.9100
0.162
69.15
Cinematic Video
NECHDR
40.59
0.9241
0.155
73.31
HDRFlow
39.20
0.9154
0.158
71.05
DeAlHDDR
40.75
0.9245
0.152
74.22
handle low-texture and blur regions more robustly. Consequently, attention mechanisms consistently outperform optical flow in challenging, nigh-motion scenarios.
Finally, as shown in Table and Table E our method outperforms the others whether the reference frame is short-exposure or long-exposure.
E COMPARISON OF COMPUTATIONAL COSTS
In this section, we provide a detailed analysis of the curve presented in Fig. I As shown Table [ when our method relies solely optical flow (i.e., 0), it achieves higher PSNR values while maintaining lower FLOPPs and inference time compared HDRFlow. As the proportion of atten- tion mechanisms increases during inference, both FLOPs and inference time dynamically increase, eventually reaching the configuration where alignment is handled entirely by attention. Compared to other lightweight networks like HDRFlow, our approach achieves higher performance while main- taining competitive computational efficiency.
In addition, provide detailed analysis of the relationship between the attention ratio and FLOPs. The relationship between the attention ratio and computational cost (FLOPs) directly proportional. The total FLOPss cost weighted sum of the costs of two operations: efficient flow- warping
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T-1
Olur Renit
T+
Clen of
LAN HDR
HDRFlw
NECHOR
Clss
GT
Chea of
LAN HDR
HDRF
NECHDR
Ous
GT
Figure C: Visual comparison on DeepHDRVideo dataset.
Our
HIDRFlow
LAN-HDR
Figure D: Visual comparison on Real-HDRV dataset.
and powerful but expensive attention. Mathematically. it can be derived as: FLOPs= N Nx Ratta) Cftww + Rattn Cattn), where: N represents the pixel number, Rattn is the attention ratio and Cfow and Cattn are the fixed costs of flow and attention per pixel, with Cattn >> Cfoow- As the equation shows, when the attention ratio Rattn increases, the overall computational cost increases monotonically, establishing direct proportional relationship. Each point in the curve in Figure corresponds to a different Ratta (controlled by our sensitivity parameter s), and the progression from left to right demonstrates this predictable increase in FLOPs.
Furthermore, we conduct an experiment with a compact model configuration (s=15) that delivers lower inference latency compared to HDRFlow. As shown in Table F our compact model still achieves better results than HDRFlow, but with fewer FLOPs and shorter inference time. Moreover, provide detailed results in Table G The results show that attention computation consti- tutes the primary bottleneck, accounting for 38.2% of the total processing time. The dual-encoder architecture also introduces notable overhead, contributing 27.6% the runtime compared single-encodee setup. In comparison, flow estimation is relatively efficient, consuming only 11.8% of the computational budget.
F EFFECT OF SELF-SUPERVISED ADAPTATION
In this section, we provide more quantitative comparisons in Table can be seen that the proposed adaptation method can bring improvements to both CLIPIQA (Wang et al. 2023) and MANIQA (Yang et al. 2022). Furthermore, only deploying Lemo could prevent parameter updates, while omitting causes self-supervised training to collapse. This occurs because processing all input frames currently operates without any architectural constraints. Moreover, we evaluate different weighting factors Atime for Ltime. From Tab. J we are higher than the other settings. Furthermore, Fig. H shows the visual comparison of adaptation methods. It can be seen that method achieves more realistic results than BracketRKF.
that when Atime is set to 1, the metrics
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Table Performance comparison between DeAlHHDR configurations and HDRFlow.
Method
PSNRT
SSIM
LPIPS
FLOPS (G)
Time (ms)
HDRFlow
32.26
0.9629
0.196
116
128
32.42
0,9629
0.201
94
0.14
32.43
0.9630
0.201
101
100
0.29
32.46
0,9633
0.198
104
108
0.43
32.48
0.9635
0.196
107
119
0.57
32.49
0,9637
0.196
111
128
0.71
32.49
0.9640
0.196
115
134
0,86
32.52
0.9640
0.194
119
138
32.53
0.9642
0.192
123
149
DeAItDDR
15
32.55
0.9644
0.192
128
152
30
32.57
0.9647
0.190
132
162
43
32.59
0.9649
0.188
137
165
57
32.60
0,9651
0.188
142
172
71
32.60
0.9653
0.185
147
186
85
32.62
0.9657
0.184
152
190
100
32.63
0.9658
0.182
159
198
X
32.65
0.9660
0.180
168
224
Input LDR images
Our Resalt
Input LDRR images
Our Result
Charatal.
LAN-HDR
HDRFlow
NECHDR
Oum
GT
Chanaral.
LAN-HDR
HDRFluw
NECHDR
Figure E: Visual comparison Cinematic Video dataset.
G MORE VISUAL RESULTS
Cinematic Video dataset (Froehlich et al.) 2014) and the Real-HDRV dataset (Shu et al. (024). Hsows the quantitative results on these datasets. It is shown that our method achieves better results than previous HDR video reconstruction methods. Moreover, Fig. C Fig. D and Fig. E show qualitative results these datasets. It can seen that our results have more details and less artifacts. Furthermore, provide more visual comparisons I and Fig. | show the qualitative results. can seen that our results have fewer artifacts and more details. Moreover, we perform cross-validation across clean datasets shown in Table K The diagonal entries (training set = test set) are the best results and are highlighted in blue; results from our dataset are second-best and shown red.
We also test our model on the DeepHDRVideo dataset (Chen et 2021).
both our synthetic dataset and our real-world dataset. Fig.
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1-1
T+1
GT
Ourput
7-1
T+1
Output
GT
T-1
T-1
Output
GT
Figure F: Full image results on our synthetic dataset. Our results preserve both the bright areas in short-exposure images and the dark areas in long-exposue images.
ROLEX
TUDOR
Madiaer
Figure G: Per-scene histograms on our dataset.
Table K: Cross-validee across clean datasets. Diagonal entries (training set set) are the best results and are highlighted in blue; results from our dataset row are second-ho -bett and shown in red.
DeepHDD Video
Real- HDRHow
Cinemaric Video
Training Set
Metrie
NECHDR
HDRFoww
DeARHDR
NECHOR
DeAIHDR
NECHDR
HDRRFlow
DeAlHDRR
DeepHDR Video
43.78
38.
SSIM
0.9558
0.9520
0.9572
0.9152
0.9178
0.9284
0.9106
0.9034
0.9216
Real HDRR
PSNR
38.05
37.30
38.50
39.23
38.98
40.04
37.50
37.10
37.95
Cinemaric Video
PSNR
38.40
37.05
38.70
37.85
37.10
38.05
40.59
40.40
40.75
SSIM
0.8971
0.8890
0.9097
0.9042
0.8872
0.9082
0.9241
0.9154
0.9245
42.78
38.
0.502
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Table J: Effect of loss terms for supe-sup real-image adaptation. "-" denotes DeAltHDR trained synthetic pairs. 'NaN' implies the training collapse.
Loma
Ltime
CLIPIQAT/ANIQQAT
0.2610/0.2716
0.2610 / 0.2716
X
NaN NaN
Atime = 0.5
0.2542 / 0.2760
Atime
0.2679 0.2774
V
Atime
2
0.2578 0.2598
www.dlgp..o.
Atime
0.2270 0.2391
Figure H: Visual comparison of adaptation
methods.
T-1
T-2
T+1
T42
Our Result
AHDRNet
Chen et al.
LAN-HDR
SCTNet
SAFNet
NECHDR
BracketIRE
HDRFlow
Ours
GT
Figure Visual comparison on our synthetic dataset.
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T-1
T-2
T-1
Our Result
AHDRNet
et al.
LAN-HDR
SCTNet
SAFNet
NECHDR
BracketIRE
HDRFlow
Ours
Figure J: Visual Comparison on our real-world dataset.
TUDOR
TUDOR
Interpolated
Figure K: Interpolation results our HDR GT videos.
25


Reviews:
[{"review_id": "xuRL1RUt0I", "reviewer": "Reviewer_TeNE", "summary": "This paper proposes DeAltHDR, a framework for HDR video reconstruction that robustly handles input degradations, such as blur and noise. A self-supervised fine-tuning strategy with randomized frame sampling enhances motion diversity and model performance. The authors also released two HDR video datasets with realistic degradation to support future studies.", "questions": "1. On which dataset was the Adaptation ablation study conducted, and what is the scale of the data used? Please clarify these details in the paper.\n2. Will the authors make the dataset and code publicly available to facilitate reproducibility and future research?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed FGMA mechanism effectively balances optical- and attention-based alignment, achieving a good trade-off between accuracy and efficiency.\n\n2. The authors also released two HDR video datasets with realistic degradation to support future studies.\n\n3. The paper is clearly written and well-organized.", "weaknesses": "1. Temporal consistency is a crucial aspect in video tasks; however, the authors did not provide corresponding video visualizations or quantitative evaluations. Additional analysis is recommended to better validate the temporal stability of the proposed method.\n\n2. Although the dataset considers blur degradation, the model itself does not appear to include specific designs targeting blur removal. The authors should analyze why the proposed method can still handle blur effectively and discuss its generalization capability. As far as I know, traditional deblurring methods often suffer from limited generalization.\n\n3. The visualization results are insufficient. It is suggested to provide more detailed analyses under different reference exposure settings: when the reference frame is long-exposure, the model should primarily address blur; when it is short-exposure, the model should focus on noise suppression. Please include corresponding visual and quantitative results to demonstrate the model’s robustness under varying exposure conditions.\n\n4. While the paper discusses computational budgets, inference speed is also an important factor. The proposed method seems to be less efficient in terms of runtime. Please further analyze the reasons for this limitation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "URK3FwEZcM", "reviewer": "Reviewer_7ouZ", "summary": "This paper proposes DeAltHDR, a method for reconstructing high-quality HDR videos from degraded LDR sequences with alternating exposures. It introduces flow-guided masked attention for efficient inter-frame alignment, and a two-stage training strategy that pre-trains on synthetic paired data and fine-tunes on real videos with self-supervision. Experiments show that DeAltHDR outperforms existing state-of-the-art methods on both synthetic and real-world datasets.", "questions": "1. The experiments show that s = ∞ achieves the best results, indicating that using only attention without optical flow gives the best performance. Then, why is the core method still flow-guided masked attention? It seems that not using flow performs better, which is confusing.\n\n2. The content shown in Figure 1 is a bit confusing. Does increasing the attention ratio lead to higher FLOPs? Is the relationship directly proportional or inversely proportional?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper has a strong motivation. The authors observe that previous methods, such as HDRFlow, overlook crucial degradations like noise and blur in HDR reconstruction. In contrast, this work explicitly addresses these issues, enabling the reconstruction of blur-free, high-quality HDR videos.\n\n2. This work achieves state-of-the-art accuracy and efficiency. Specifically, it delivers higher reconstruction quality than previous methods like HDRFlow, while maintaining comparable efficiency to HDRFlow, the fastest existing method.\n\n3. The paper is well-written and the experiments are comprehensive and thorough.", "weaknesses": "1. In the introduction, the authors mention that optical-flow-based alignment cannot handle long-exposure blur, while attention-based methods can. However, the paper does not provide a detailed explanation for this claim. I am curious why attention-based methods can restore blur as shown in Figures 3, 4, and 5.\n\n2. The self-supervised fine-tuning is very similar to BracketIRE, which limits the novelty and contribution of this part of the work.\n\n3. This paper aims to address noise and blur in HDR reconstruction, but most of the results mainly demonstrate the handling of blur, and there is a lack of results showing the effect on noise.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nIQUshPGPn", "reviewer": "Reviewer_9w9D", "summary": "This paper tackles the problem of reconstructing HDR videos from alternating-exposure LDR sequences, which is a realistic yet challenging task due to varying exposure, motion, and noise artifacts.\nThe proposed DeAltHDR is a framework featuring a flow-guided masked cross-attention module that selectively attends to motion-aligned regions across alternating frames.\nThis design aims to improve robustness to motion blur, ghosting, and exposure inconsistencies.\nThis paper  further introduces a controllable attention ratio to trade off performance and inference cost.\nExperiments on both synthetic and real datasets demonstrate superior quantitative and qualitative HDR reconstruction results over several baselines.", "questions": "1. As shown in Fig. 5, our method outperforms the “w/o Adaption” baseline in terms of sharpness. Based on the definition of the Adaption module, it seems primarily designed to improve reconstruction consistency when different frames are used as input. However, it is unclear why this module also affects reconstruction sharpness.\n2. Which specific component of the proposed method contributes most to handling the noise and blurriness in the input LDR images? The paper does not clearly describe any design specifically targeting in-frame blurriness or noise.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Realistic Problem Setup: Addressing alternating-exposure HDR reconstruction is practically valuable and closely aligned with real camera capture settings.\n2. Novel Attention Design: The flow-guided masked cross-attention module and controllable attention ratio are well-motivated and effectively balance accuracy vs. computational cost.", "weaknesses": "1. Evaluation Metrics: While the proposed method outperforms baselines on reconstruction metrics, it does not include temporal consistency metrics. For video HDR tasks, flicker suppression is crucial, making temporal consistency metrics essential.\n2. Synthetic Dataset: The synthetic dataset uses RIFE for interpolation. However, existing interpolation methods are typically designed and trained for 8-bit videos, which may not be suitable for HDR video inputs, potentially resulting in inaccurate HDR frame interpolation.\n3. Training Data for Comparison Methods: Since the proposed method is trained on a new dataset that includes blurriness and noise in the input LDR images, the comparison methods should also be trained on datasets with similar characteristics to ensure a fair comparison.\n4. Absence of Video Results: The paper does not provide qualitative or quantitative video results. Since the method is designed for video HDR reconstruction, demonstrating video sequences with temporal consistency and flicker-free performance is crucial. Without video results, it is difficult to verify the practical effectiveness of the method in real-world scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KOiFDQaTUA", "reviewer": "Reviewer_qc8j", "summary": "This paper proposes DeAltHDR, a novel framework for high-quality HDR video reconstruction from degraded sequences. First, it proposes a flow-guided masked attention that leverages optical flow for a dynamic sparse cross-attention computation, achieving improved performance while maintaining efficiency. Its controllable attention ratio allows for adaptive inference costs. Second, to enable  practical deployment, it introduces a two-stage training approach: the model is first pre-trained on the newly introduced synthetic paired dataset and subsequently fine-tuned on unlabeled real-world videos via a proposed self-supervised method. Experiments show the proposed method outperforms state-of-the-art methods.", "questions": "- No supplementary video results are provided, making it difficult to assess temporal consistency. Please clarify.\n- The contribution and effectiveness of the proposed dataset are insufficiently validated; stronger justification or ablation evidence is needed.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- A flow-guided masked attention for efficient inter-frame alignment, where the attention ratio can be dynamically adjustable for adaptive inference cost.\n- A motion-enhanced self-supervised fine-tuning approach is proposed to improve the reconstruction effect on real-world videos.\n- A synthetic and real-world datasets with rich scenes are constructed. The proposed method outperforms the state-of-the-art methods on multiple datasets.", "weaknesses": "- The paper shows many frame-level qualitative images but provides no video results or links to video demos to assess temporal consistency. For a video reconstruction paper, absence of video demos is a major omission. This omission reduces my confidence in the method’s claimed temporal stability and real-world applicability.\n- The paper introduces both a synthetic and a real-world dataset but provides only limited visualizations and statistics . This makes it hard to judge how challenging the real captures are (exposure range, highlight clipping). It would be better add per-scene histograms, luminance range (DR) for the real-world set, sample raw/RGB patches at multiple exposure levels, and a table summarizing scene types and illumination conditions.  \n- Moreover, the real dataset uses only one phone model (iPhone 16 Pro Max). That limits claims of real-world generalization across camera sensors (different saturations, noise profiles, ISP pipelines).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)