Learning Differential Pyramid Representation for Tone Mapping
Authors: Qirui Yang
Yinbo Li
Yihao Liu
Peng-Tao Jiang
zhangfangpu
cheng qihua
Huanjing Yue
Jingyu Yang
Venue: NeurIPS 2025 poster ()
Keywords: Computational Photography
Tone Mapping
Primary area: applications

Abstract: Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. Existing tone mapping methods operate on downsampled inputs and rely on handcrafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textures and structural fidelity in complex HDR scenes. Furthermore, most methods lack an effective mechanism to jointly model global tone consistency and local contrast enhancement, leading to globally flat or locally inconsistent outputs such as halo artifacts. We present the Differential Pyramid Representation Network (DPRNet), an end-to-end framework for high-fidelity tone mapping. At its core is a learnable differential pyramid that generalizes traditional Laplacian and Difference-of-Gaussian pyramids through content-aware differencing operations across scales. This allows DPRNet to adaptively capture high-frequency variations under diverse luminance and contrast conditions. To enforce perceptual consistency, DPRNet incorporates global tone perception and local tone tuning modules operating on downsampled inputs, enabling efficient yet expressive tone adaptation. Finally, an iterative detail enhancement module progressively restores the full-resolution output in a coarse-to-fine manner, reinforcing structure and sharpness. Experiments show that DPRNet achieves state-of-the-art results, improving PSNR by **2.39 dB** on the 4K HDR+ dataset and **3.01 dB** on the 4K HDRI Haven dataset, while producing perceptually coherent, detail-preserving results. Demo available at [DPRNet](https://xxxxxxdprnet.github.io/DPRNet/).

Learning Differential Pyramid Representation for Tone Mapping
Qirui Yang
Yinbo Li
Yihao Liu
Peng-Tao Jiang
Fangpu Zhang
Qihua Cheng
Huanjing Yue Jingyu Yang 1=
School of Electrical and Information Engineering, Tianjin University, China 2 Shanghai Artificial Intelligence Laboratory 3 vivo Mobile Communication Co., Ltd {yangqirui, liyinbo, zhangfp, huanjing. yue, yjy)atju. liuyihao mails.ucas.ac.cn, pt.jiang vivo.com
Abstract
Existing tone mapping methods operate on downsampled inputs and rely on hand- crafted pyramids to recover high-frequency details. These designs typically fail to preserve fine textures and structural fidelity in complex HDR scenes, Furthermore, most methods lack an effective mechanism to jointly model global tone consistency and local contrast enhancement, leading to globally flat or locally inconsistent outputs such as halo artifacts. We present the Differential Pyramid Representation Network (DPRNet), an end-to-end framework for high-fidelity tone mapping. At its core is learnable differential pyramid that generalizes traditional Laplacian and Difference-of- ofaaassi pyramids through content-aware differencing operations across scales. This allows DPRNet to adaptively capture high-frequeng, variations under diverse luminance and contrast conditions. To enforce perceptual consis- tency, DPRNet incorporates global tone perception and local tone tuning modules operating on downsampled inputs, enabling efficient yet expressive tone adaptation. Finally, iterative detail enhancement module progressively restores the full- resolution output in a coarse-to- -fne manner, reinforcing structure and sharpness. Experiments show that DPRNet achieves state-of-ther -he- results, improving PSNR by 2.39 dB on the 4K HDR+ dataset and 3.01 dB on the 4K HDRI Haven dataset, while producing perceptually coherent, detail-preserving results. We provide anonymous online demo at DPRNet.
Testing on 480p HiRe delaset
Testing on 480p HDRI Haver dataset
Texting
Sunvey cataset
Testing on UVTN cotlatet
052e
22.33
COTTE NE135e
40
0.911
(e)
28.55
E.0
0,622
0,202
23.28
800
(ba)
Inputt
Sepl LTT
COTF
Outss
Figure 1: (a) Our DPRNet provides remarkable performance gains the four datasets. (b) Existing tone mapping methods suffer from detail loss and halo artifacts.
*Corresponding author: This work is supported by the National Natural Science Foundation China under Grants 62231018 8 and 62472308.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
High dynamic range (HDR) imaging enables more faithful visual reproduction by capturing the wide luminance and color variations present in natural scenes. However, since most consumer displays support only a limited dynamic range, HDR content must be compressed via tone mapping to retain perceptual quality on standard devices. This compression task is inherently ill-posed: it requires reconciling global luminance consistency with the preservation of fine local structure and detail.
Many recent tone mapping methods adopt low-resolution pipeline: they downsample the HDR input, perform tone compression at reduced scale, and then upsample the output to full resolution [1; 2: 3]. This strategy reduces computational overhead and allows the use of compact models. However, it introduces an intrinsic information bottleneck: once high-frequency details, such as fine edges, textures, and localized contrast, are lost during downsampling, they cannot be faithfully recovered by simple interpolation or shallow upsampling networks. This leads to visually flat results, especially in high-contrast light regions, where subtle structural cues are critical.
To mitigate these losses, some methods [4; 5; 6] attempt to reintroduce high-frequency components using multi-scale decompositions,
such Laplacian or Gaussian pyramids [7; 8;
91. These pyramids are handcrafted and fixed, applying the same filters across all scenes regardless of content, luminance dynamics, or semantic structure. Consequently, the ex- tracted high-frequency representations are of- ten misaligned with true image gradients or details. This mismatch can result in incom- plete detail reconstruction and halo artifacts, particularly in HDR scenes where local contrast varies dramatically across spatial regions (see Fig. 2).
Enery:: 45.8M
Energy: 86 38.1M
LP
LDP
GT
Figure 2: Example of high-frequenn information
extracted from the Laplacian pyramid (LP) and our LDP. We can observe that our LDP extracts high- frequency information more effectively than LP.
Beyond detail recovery, another core challenge lies in achieving tone consistency. Global tone mapping methods ensure uniform luminance but often suppress local contrast [10; 2]. while local methods enhance fine detail at the risk of overexposure or inconsistency [11; 12]. Most learning- tone mapping networks still treat global and local tone mapping as independent problems [13; 4], making it difficult to produce outputs that are both globally coherent and locally expressive.
In summary, current tone mapping frameworks face three fundamental limitations: (1) High-frequency degradation due to downsampling and weak reconstruction, (2) Limited adaptability of traditional multi-scale decompositions under diverse HDR conditions, and (3) Disjoint modeling of global tone control and local contrast adjustment, leading incoherent perceptual results. Our key insight is that addressing these challenges requires a unified, learnable representation that bridges frequency-awant detail extraction with content-adaptive tone mapping.
To address these challenges, we propose DPRNet, an end-to-end Differential Pyramid Representation Network designed to tackle tone mapping comprehensively. At its core is novel Learnable Differen- tial Pyramid (LDP) that generalizes classical Difference-of-Gaussee
pyramids with adaptive filtering and differencing, enabling robust high-frequency extraction across luminance scales. To enforce tonal consistency, we introduce a GllobalLLocal Collaborative Tone Mapping mechanism consisting of: (1) Global Tone Perception (GTP) module that processes downsampled inputs for scene-awee luminance adjustment, and (2) Local Tone Tuning (LTT) module that refines patch-wise tone via learned 3D LUTSs. Finally, Iterative Detail Enhancement (IDE) module progressively integrates high-frequency components in a coarse-to-fine manner to reconstruct structurally faithful outputs. To facilitate robust training and benchmarking, also introduce new tone mapping dataset, HDRI Haven, that spans a wide range of real-world HDR scenes and lighting conditions.
In conclusion, the highlights of this work can be summarized into three points:
(1) We propose DPRNet, unified tone mapping framework that integrates global tone perception, local tone adaptation, and figh-frequency detail reconstruction.
(2) We design Learnable Differential Pyramid for content-adaptive multi-scale detail extraction, and an Iterative Detail Enhancement module for progressive fidelity refinement.
(3) We introduce new benchmark. HDRI Haven, enabling effective training and generalization.

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(4) DPRNet achieves state-of-the-an performance, with 2.39 dB PSNR improvement on the 4k HDR+ dataset and 3.01 dB on the 4k HDRI Haven dataset, while producing perceptually coherent and detail-preserving results.
2
Related works
2.1 Learning-based Tone Mapping Methods
Recent advancements in tone mapping have leveraged deep learning techniques [6; 14] to address the nonlinear mapping from HDR to LDR images. Hou et al. [15] applied CNNs to tone mapping tasks, establishing foundation for subsequent research. He et al. [10] developed conditional sequential retouching network for effective image retouching. Similarly, Cao et al. [16], Rana et al. [17], and Panetta et al. [18] explored generative adversarial networks (GANs) for pixel-precise mapping. Despite these efforts, issues such as halo effects, noise, and local area processing remain challenging. To combat these issues, several recent works have introduced innovative solutions. For example, Hu et al. [19] combined tone mapping with denoising, incorporating a discrete cosine transform module for noise removal and improved image quality. Zhang et al. [20] manipulated tone in the HSV color space, which significantly reduced halos and preserved detail. Additionally, unsupervised and semi-supervised approaches have gained attention for their ability handle large datasets without labeled data. Cao et al. [21] proposed an unsupervised HDR image and video tone mapping method using contrastive learning, which effectively captures the intrinsic properties of HDR images without explicit supervision. This method demonstrates strong generalization capabilities across different datasets. Zhu et al. [22] proposed Diffusion-based [23] zero-shot tone mapping framework that utilizes shared structural knowledge to transfer pre-trained mapping models from the LDR domain to the HDR domain without paired training data. However, Diffusion- methods are difficult to deploy effectively on mobile devices, limiting their range of applications.
For real-time applications. efficiency is a critical concern. Wang et al. [24] developed fast global tone mapping algorithm for HDR compression, achieving real-time performance with minimal computational resources. Similarly, Zhang et al. [25] introduced a real-time semi-supervised deep tone mapping network that balances accuracy and speed, making it suitable for practical applications. In hardware implementations, latency and resource constraints pose additional challenges. Liang et al. [12] proposed a low-latency noise-aware tone mapping operator with a locally weighted guided filter, designed specifically for hardware implemeentatio.. This approach ensures high-quality tone mapping while maintaining low latency and computational efficiency. Despite the progress made by these learning-base based methods, most of them are either global or local mappings, struggling to achieve satisfactory tone mapping results. To address this, our proposed Differential Pyramid Representation Network (DPRNet) combines global and local tone mapping while preserving fine details, offering a comprehensive solution to the challenges in HDR to LDRR conversion.
2.2 3D LUT-based Enhancement Methods
In pursuit of computational efficiency and performance, emerging tone mapping methods employed 3D LUT [26; 27; 4] to render image brightness and color. Zeng et al. [2] developed an adaptive 3D LUT prediction network that merges multiple foundational 3D LUTs. Zhang et al. [3] presented compressed representation of 3D LUTs, reducing parameters. Wang et al. [28] studied pixel-level fusion based on 3D LUTs, extending the method to spatially aware variant. Yang et al. [29] proposed an effective ICELUT for extremely efficient edge inference. Yang et al. [30] introduced the AdaInt mechanism, which adaptively learns con-uniform sampling intervals in the 3D color space for more flexible sampling point allocation. Furthermore, Yang et al. [1] proposed SepLUT, addressing the shortcomings of ID LUTs in color component interaction while alleviating the memory consumption issues of 3D LUTS.. Despite these efforts, these methods necessitate an initial down-sampling step to reduce the network computation, and in the case of 4K images, even need 16 times down-sampling. As result, this leads to loss of image details and degradation of image quality.
3 Methods
3.1
Motivation
While tone mapping has advanced considerably, most existing methods prioritize computational efficiency by processing low-resolution representations of HDR images. These methods apply tone mapping to downsampled inputs, then upsample the outputs back to the original size. However, this
3

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Light Unitet
Brightrass
Connodt
Exposare
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Globel Contect Entbedkinng
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Globel Tone Perception (GIP)
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The overall pipeline of our DPRNet. It includes the proposed Learnable Differential Pyramid (LDP), Iterative Detail Enhancement (IDE), Global Tone Perception (GTP), and Local Tone Tuning (LTT). The LDP, in synergy with the IDE, is responsible for extracting and enhancing the high-frequency details. The GTP collaborates with LTT to achieve globally consistent yet locally fine-grained tone mapping.
Figure 3:
approach often compromises high-frequency fidelity, leading to blurred textures, detail loss, and halo artifacts, particularly in high-contrast or brightly lit areas. To mitigate such losses, prior works [4; 31; 32] have incorporated multi-scale decomposition frameworks such as Laplacian pyramids [7] to recover fine details. Nevertheless, these pyramids rely on fixed, handcrafted filters that lack adaptability to the diverse luminance distributions and complex structures of HDR scenes. As a result, the high-frrquency components extracted using such methods are often unstable or incomplete, ultimately resulting in over-smoothed reconstructions. This highlights a critical need for a learnable, adaptive mechanism robustly extract and preserve nighh-freuency features from HDR inputs.
In parallel, effective tone mapping must also maintain tonal harmonization across the image. Global tone mapping [10; 2] enhances perceptual consistency, but often neglects localized details. In contrast, local methods [11; 12] refine contrast in specific regions, yet lack contextual awareness, which can lead to visual inconsistencies. The lack of integration between these two perspectives results in tone mapping systems that are either globally coherent but locally flat or locally sharp but globally inconsistent. Thus, the central challenge is to design unified tone mapping network that effectively captures high-freyuency detail while jointly modeling both global and local tonal coherence.
3.2 Framework Overview
To address these challenges, propose DPRNet, end-to-end network for HDR tone mapping that simultancously achieves structural fidelity and tonal coherence. As illustrated Fig. 3, DPRNet is composed of four main components: 1) the Learnable Differential Pyramid (LDP) for adaptive extraction of multi-scale high- figh-freeueny.. 2) the Global Tone Perception (GTP) module to perform global luminance adaptation based image-aware, 3) the Local Tone Tuning (LTT) module to refine regional contrast through patch-aware 3D LUTs, and 4) Iterative Detail Enhancement (IDE) module for progressive refinement of high-frequency content.
Given an HDR image To- The LDP first extracts multi-scale high-frequency components from X. The GTP module then performs global tone mapping on a low-resolution version of the input (L3), while LIT applies localized adjustments to enhance regional contrast. Finally, IDE progressively integrates the high- frequency features into the tone-mapped image, refining detail from coarse to fine scales.
#333 our goal is produce a perceptually faithful LDR output
3.3 Learnable Differential Pyramid
High-frequency extraction is crucial for preserving detail in HDR images, which often contain rich textures and wider luminance range. To this end, we propose the I.DP, trainable, multi-scale decomposition module inspired by the principles of differential filtering and scale invariance [33; 34].

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LDP exhibits two desirable properties: 1) it produces stable feature representations under scale and illumination changes, and 2) it captures edges, cornrrs and texture patterns effectively through learned difference operations.
The pipeline begins by convolving the input image X with 3 3 kernel generate initial feature map Fo. A lower-resolution map F is then produced via four convolutions and max pooling. To model the Difference of Gaussian (DoG) [35; 36], we compute the differences between the outputs of these four convolutions, resulting in a set of differential features Do. These differential features are then concatenated and passed through a residual block to produce the first nigh-freuency output Hy. This process is repeated for three scales, yielding differential pyramid:
X (Ho: H2),
W. with progressively reduced spatial resolutions from H x W to H. x 4. These high- high-frequency features are used downstream restore lost
detail in the final image.
3.4 Global-Local Collaborative Tone Mapping
An effective tone mapping system must address both global perceptual consistency and local contrast enhancement. To this end, we propose a collaborative tone mapping strategy that integrates a GTP module and an LTT module. The GTP collaborates with LTT to achieve image-wide tonal alignment yet locally coherent tone mapping. Their combination enables DPRNet to handle complex HDR scenes with rich structure and varying illumination.
Global Tone Perception (GTP). GTP performs global tone mapping the downsampled input Lg R W. x3 to reduce computational load while capturing scene-wide illumination. It comprises two parts: condition network C(-) that extracts global context features, and a modulated convolutional core F.) that adapts global information (brightness, contrast, exposure) based on the input context. The condition network C(-) consists of three convolutional layers, which extract global features, and apply global average pooling (GAP) to produce a condition vector z:
GAP(C(L3)).
This vector is then used generate per- per-layer modulation parameters via three fully connected layers:
70
( 2, g(0) z, = 1,2,3,
where ! wi( E _dxC are learned weight matrices that generate modulation vectors for each layer and Cj is the number of channels at layer l. Each layer's output is modulated as:
F = ReLU (WO) p(-1)) . (00) + 0000 + +F((-))
(4)
The final output is the globally tone mapped image (3) = L.C
This conditional modulation allows GTP to adapt global information based on the scene context, leading to improved brightness and color consistency across varying global lighting conditions.
Local Tone Tuning (LTT). While GTP ensures global alignment, local details such as highlights, shadows, or complex textures still require region-specific enhancement. To address this, we introduce LTT, a region-aware tone adjustment module based on learned 3D LUTSs. We first resize Lg and extract contextual features using lightweight encoder E()):
FCF = E(L) E Bx6xxx4
(5)
where the lightweight encoder consists of three convolutional layers, LeakyReLU activation, in- stance normalization, and adaptive pooling. This contextual features FCE is reshaped into N2 local descriptors with dimensionality d 6:
{er E R 1==
(6)
where N = 4 in our setting, Each local descriptor e; is passed fully connected network (denoted FC) to predict weights over bank of R basis LUTs:
w, FC(e.) R
5

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Table 1: Quantitative results of TM methods on the HDR+ dataset. The "N.A." " result is unavailable due to insufficient GPU memory. The "*" symbol indicates that the results are adopted from the original paper (some are absent ("/")) due the unavailable source code. Metrics with and + denote
higher better and lower better. The best and second results are in red and blue, respectively.
HDR+ (480p)
HDR+ (original 4K)
Method
AParams
PSNRT
SSIM
TMQIT
LPIPS]
ZEE
PSNR
SSIMI
TMQUI
LPIPST
UPE [37]
999K
23.33
0.852
0.856
0.150
7.68
21.54
0.723
0.821
0.361
9.88
HDRNet [38]
482K
24.15
0.845
0.877
0.110
7.15
23.94
0.796
0.845
0.266
6.77
CSRNet [10]
37K
23.72
0.864
0.884
0.104
6.67
22.54
0,766
0.830
0.284
7.55
DeepLPF [9]]
1720K
25.73
0.877
0.073
N.A.
N.A.
N.A.
LUT [2]
592K
23.29
0.855
0.882
0.117
7.16
21.78
0.772
0,850
0.303
9.45
LPTN [3]
616K
24.80
0.884
0.885
0.087
8.38
24.05
0. 0.807
0,839
0.207
9.04
CLUT [3]
952K
26.05
0,892
0.886
0,088
5.57
24.04
0.789
0.848
0.245
6,78
SLUT [28]*
4520K
26.13
0,901
1
0,069
5.34
23,98
0.789
0.242
6.85
Sepl.UT
120K
22.71
0.833
0.879
0,093
8.62
21.87
0.731
0.842
0.220
9.52
LLFLUT [4]
731K
26.62
0.907
/
0,063
5,31
25.32
0.849
0.149
6.03
CODFFormer [40]
12232K
27.33
0.941
0.882
0.028
5.79
N.A.
N.A.
N.A.
N.A.
N.A.
CoTF [41]
310K
23.78
0.882
0.876
0.072
7.76
23.44
0.818
0.866
0.175
8.01
RECNet
39K
0.905
0.883
0.063
7.53
23.26
0.818
0.865
0.148
8.54
Ours
212K
27.95
0.931
0.888
0.033
5.72
27.71
0.920
0.870
0,066
5,31
The final LUT for patch is constructed as a weighted sum:
LUT, = Σ w B(7))
(8)
where LUT; E 3xV for patch-wise contrast enhancement, B(r) denotes the r-th basis LUT (shared across patches), V is the number of discretization points per channel (typically V 9), and 3 corresponds to the RGB channels. To apply each LUT,, we use a differentiable trilinear interpolation operator (-)):
T₁ I(P: LU,;,LUT;)
(9)
where P. E x is the i-th image patch and T, is the rdne-adjusted patch. To ensure spatial smoothness, bilinear blending is applied patch boundaries during reassembly of the full image T3.
Collaborative Mapping Strategy. By applying GTP and LTT sequentially, DPRNet achieves globally consistent yet locally expressive tone mapping. GTP sets a stable tonal foundation, while LTT introduces fine-grained control over regional contrast and detail, resulting in images that are both perceptually coherent and rich in local structure.
3.5 Iterative Detail Enhancement
To restore fine details lost during downsampling tonal transformations, DPRNet incorporates the IDE module. IDE integrates the previously extracted high-frequency features XHF into the tone-mapped image Ta through progressive, coarse-to-fine reconstruction process. At each scale the current output T, is upsampled and concat with the corresponding high-frequency map H,,
F Pa-1 = Concat(UppTT, Hn-1),
(10)
where Up(-) denotes the upsampling operation. A refinement network R-n-1)(-) then predicts a residual correction, added to the upsampled input:
Tn-1 = Up(T,,) + R ((m-))
(11)
where R(-) consists of residual blocks. This design enables adaptive integration of HF textures across multiple scales, yielding high-quality LDR outputs with enhanced sharpness and structural fidelity.
3.6 Loss Functions
To ensure accurate reconstruction of the image, we propose multi-loss functions that guides both pixel-level and scale-wise image enhancement, Our optimization jointly incorporates pixel-wise reconstruction loss LRe, structural similarity loss Lesim- figh-frequency loss LHF. and perceptual loss Lp. To summarize, the complete objective of our proposed model combined as follows:
Liotal
Lae + B Lysim + LHF + 1) Lp-
(12)
where a, B, n. and n are the corresponding weight coefficients. More details are in the appendix.

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(A)
Imput
HDRNEt
CSRNet
3D LUT
CLUT
-1
LPIN
COTF
SepLUT
OuIS
GT
30
(B)
1000
0000
20
Input
HDRN
CSRNN
3D LUT
CLUT
19
1013
0000
LPIN
CoTF
SepLUT
Ours
GT
Figure 4: Visual comparisons between our DPRNet and the other methods on the 480p HDR+ dataset. The error maps in the upper left corner provide clearer insights into performance differences.
Table 2: Quantitative results on the HDRI Haven dataset. The "N.A." result is not available due to insufficient GPU memory.
HDRI Haven (480p)
HDRI Haven (original 4K)
Method
PSNR
SSIM
TMQ
LPIPS.
PSNRT
SSIM
TMQI
LPIPS
A E..
UPE [37]
23.58
0.821
0.917
0.191
10.85
21.62
0.776
0.875
0.232
13.17
HDRNet [38]
25.33
0.912
0.941
0.113
7.03
23.61
0.899
0.890
0.143
8.19
DeepI [39]
24.86
0.939
0.948
0.077
7.64
N.A.
N.A.
N.A.
N.A.
N.A.
CSRNet [10]
25.78
0.872
0.863
0.875
0.174
LUT [2]
24.52
0.846
0,912
0.171
21.80
0.823
0.849
0.197
8.49
CLUT [3]
24.29
0.836
0.908
0.169
7.08
22.32
0.765
0.842
0.281
9.31
LPTN [3]]
26.21
0.941
0.954
0.113
8.82
23.83
0.899
0.932
0.158
10.09
SepLUT [1]
24.12
0.854
0.915
0.165
8.03
22.79
0.838
0.859
0,180
9.11
CODEFormer [40]
29.23
0.967
0.956
0.021
4.81
N.A.
N.A.
N.A.
N.A.
N.A.
CoTF [41]
26.65
0,935
5.84
0.891
0.911
0.156
7.19
RECNet [42]
27.50
0.941
0.955
0.054
6.66
25.29
0.898
0.907
0.126
7.06
Ours
30.47
0.966
0.959
0.024
4.62
28.30
0.946
0.938
0.034
5.43
4 Experiments
4.1 Experimental Settings
Datasets. We evaluate our method four diverse datasets: HDR+ Burst Photography [43], HDRI Haven, HDR Survey [44], and the UVTM video dataset [21]. The HDR+ dataset, commonly used for HDR reconstruction and tone mapping research, includes 675 image sets for training and 248 for testing at both 480p and 4K resolutions, following the preprocessing method of Zeng et al. [2]. For HDRI Haven, collected 570 HDR image pairs from the website HDRI Haven and created corresponding ground truths using several software and toning tools combined with selection algorithms.. These images cover a wide range of scenes, such as indoor and outdoor, including natural light/artificial light, multi-contrast, sunrise/sunset, urban/nature, daytime/nightinn, and so on. We select 456 image sets training and for testing. The HDR Survey dataset consists of 105 real HDR images, with ground truth, and is one of the benchmarks for HDR tone mapping evaluations [21; 22]. Lastly, the UVTM video dataset, also with no ground truth, includes 20 real captured HDR videos. Note that the HDR Survey and UVTM video datasets are for real-world testing only.
7

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(A)
Input
HDRN
CSRNat
3D LUT
CLUT
LPTN
COTF
SEpLUT
Ours
GT
B
Input
HDRNet
CSRNet
3D LUT
CLUT
LPTN
COIF
SepLUT
OUBS
GT
Figure 5: Visual comparisons between our DPRNet and the state-of-the-arr methods on the 480p HDRI Haven dataset (Zoom-in for best view).
Implementation Details. We evaluate model performance in five dimensions: pixel accuracy (PSNR), structural integrity (SSIM), mapping quality (TMQI [45]), perceptual quality (LPIPS [46]), and color fidelity (&E E [47]). We implement the model using PyTorch on an RTX 3090 GPU and optimize using the AdamW optimizer [48], with 31 = 0.9, 82 0.99, and initial learning rate of 1 10-4.
4.2 Comparison with State-of-the-Arts
Quantitative Comparison. We compare DPRNet with several state-of the-art tone mapping methods that use HDR+ dataset as benchmarks, including LLFLUT [4], SepLUT [1]. CLUT [17], sLUT [28]. LUT [2], and HDRNet [38]. We further add comparison with image enhancement and expo- sure correction methods, including CoTF [41], RECNet [42], ZS-Diffusion [22], IVTMNet [21], CODEFormer [40], UPE [37], CSRNet [10], DeepLPF [39], LPTN [31]. To ensure a fair comparison, we follow the results in the latest published papers [4] and use publicly available source code and recommended configuration for training other methods.
Our method shows significant improvement over the most recent works, LLFLUT (NeurIPS 2023 [4]) and RECNet (AAAI 2024 [42]). Tab. reports the results for the HDR+ dataset both 480p and 4K resolutions. DPRNet demonstrates robust performance at higher resolutions, with performance boost in PSNR of 2.39 dB compared L.F.UTT at original resolution (4K). the HDRI Haven dataset, as shown in Tab. 2, DPRNet achieves over 3 dB improvement at 4K resolution, reflecting the fine balance between global and local tone mapping our framework. Compared to previous methods, DPRNet demonstrates superiority with acceptable parameters (212K), further demonstrating the superiority our approach.
Qualitative Results. Visual comparisons between DPRNet and state-of-the-art methods are shown Fig. 4, Fig. 5, and Fig. 7. To better highlight the performance differences, we provide error maps where red areas represent larger discrepancies, and blue areas indicate closer alignment to the target image. These visual comparisons consistently show that DPRNet produces superior results in terms of local detail preservation, color fidelity, and structural integrity across HDR+ and HDRI Haven datasets. For example, Fig. 4, DPRNet excels in local detail processing while maintaining color accuracy, demonstrating a better balance between global and local regions. In contrast, previous methods either result in color distortion (e.g.. 3D LUT and SepLUT in Fig. 4(A), LPTN and CSRNet in Fig. 5(A)) or fail to map local area (e.g., HDRNet, CSRNet, COTF, SepLUT in Fig, 4(B), CLUT, SepLUT in Fig. 5(B). For real-world scenes, as shown in Fig. 7, our method faithfully reconstructs fine high-frequency textures while ensuring precise color reconstruction and vivid color saturation.
4.3 Ablation Studies
Effectiveness of Specific Modules. To evaluate the impact of each key component in our proposed DPRNet, we conduct ablation experiments by systematically removing or modifying specific modules: LTT. GTP, LDP, and IDE. The results, summarized in Tab. highlight the contribution of each

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w/ TM
w/o GTP
w/o LTT
w/ GTP&LTT
Figure 6: Visual results of ablation study on GTP and LTT components.
module the overall performance. From the table, we observe that removing the LTT module (Variant #1) results the PSNR drops to 26.36 dB from 27.95 dB (Variant #5), and the SSIM decreases from 0.931 to 0.921, showing that the local adjustment of tonal details crucial for improving both the structural integrity and visual quality of the cone-mapped image. Removing GTP (Variant #2) also leads to a drop in performance. This indicates that global luminance adaptation plays key role in ensuring the image maintains overall tonal consistency across varying lighting conditions, even though the local refinement (LTT) still contributes significantly.
As shown in Fig. 6, the collab- oration of GTP with LTT yields promising visual quality in both global and local enhancement in this challenging case. When the LDP is removed (Variant #3), the performance shows more sub- stantial drop in PSNR (25.90 dB), which reflects loss in detail fidelity. The PSNR degradation demonstrates the importance of the differential pyramid in capturing high-frequency details essential for fine texture preservation. Re- moving the IDE module (Variant #4) also leads to significant decline in performance. Results demonstrate the importance of progressively refining high-frequency details for structural integrity. When all modules are combined (Variant #5), DPRNet achieves the highest performance. These results confirm the complementary roles of each component in enhancing the perceptual quality, structural detail, and tonal consistency of the final output.
Table 3: Ablation studies of key components.
Variants
LTT
GTP
LDP
IDE
PSNR1
SSIM
TMQI
LPIPS|
AEL
x
/
26.36
0.921
0.887
0.042
6.50
#2
v
x
26.17
0.913
0.885
0.043
6.73
#3
x
25.90
0.912
0.889
0.076
8.61
#4
x
25.35
0.872
0.877
0.071
6.85
27.95
0.931
0.888
0.033
In summary, the ablation study highlights the effectiveness of each component in DPRNet. Removing any single module results in a noticeable decrease in performance. The full model, integrating all modules, achieves the best overall performance, underscoring the importance of the collaborative interaction between LTT, GTP, LDP, and IDE in our framework.
Ablation Study on Loss Functions. We conduct an ablation study on the loss function to evaluate the im-
Table 4: Ablation study the loss function.
Variants
Line
losin
Las
Lp
PSNR
SSIM
pact of the loss function on training DPRNet. The re-
sults, summarized in Tab. 4, provide insights into the contribution of each loss the overall performance. The ablation study reveals that each component of
#1 #2
27.85
0.929
/
/
27.74
0.927
x
27.76
0.925
27.95
0.931
the loss function contributes to improving different aspects of the mone-mapped image. The Lge loss ensures pixel-wise accuracy, the Lysim loss enhances structural similarity, the LHF loss preserves fine details, and the Lp loss improves visual quality. The best performance is achieved when all components are combined, underscoring the importance of each loss in achieving high-quality tone mapping.
HDR+ or HDRI Haven Datasets better? To
Table 5: Ablation study on the datasct.
evaluate the generalization capability of our HDRI Haven dataset, we conducted an ablation study comparing its performance against the HDR+ dataset. Specifically, we trained our model on both datasets and tested the performance on two real-world tone mapping datasets: HDR Survey [44] and UVTM [21]. We used TMQI as the evaluation metric to assess the perceptual quality and consistency of the tone mapping results. As shown in Tab. 5, when trained on the HDR+ dataset, DPRNet achieves TMQI scores of 0.8 1.8157 on the HDR Survey dataset and 0.8476 on the UVTM dataset. However, when trained on the HDRI Haven dataset, DPRNet significantly outperforms this, achieving TMQI scores of 0.9245 and 0.9540, respectively. These results demonstrate that
Method
Training Dataset
HDR Survey [44]
Testing Dataset
UVTM [2]]
HDR+ [431 HDRI Haven
0.8157
0.8476
0.9245
0.9540

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Input
CSRNet
HDRNet
LUT
CLUT
CoTF
SepLUT
Ous
Figure 7: Visual comparisons between our DPRNet and the other methods.
HDRI Haven provides better generalization and is more robust for real-world HDR tone mapping, confirming its superior versatility and performance.
4.4
Validation of Generalization
We evaluate the generaliza- tion performance of the pro- posed model on the HDR Sur- vey dataset [44] and UVTM video dataset [21] using the no- reference metric TMQI as the evaluation metric. We use the models trained on the 4K HDRI Haven dataset and report the results Tab. 6. Our proposed model achieves the highest TMQI scores and significantly outperforms the other methods. Compared with the ZS-Diffusion [22], DPRNet achieves notable improvements of 0.033, demonstrating its superiority handling HDR content across diverse scenarios and datasets. Visual results in Fig. 7 show the superiority of the generalization performance of our model.
Table 6: Validating generalization third-pan pard-paatt datascts, including HDR Survey and UVTM vidco datasets. IVTMNet and ZS-Diffusion report results from the original paper.
SepLUT
2S-Diffusion
Survey
IMQQ TMQI
0.9541
4.5
User Study
To evaluate the overall performance of the photorealism and visual quality, we perform user study based on hu- man perception. We conducted subjective evaluation of 25 users with varying experiences with HDR processing. The participants are asked to rank four tone-mapped im- ages (HDRNet [38], CLUT [3], GT, and Ours) according to the aesthetic visual quality. 50 images are randomly selected from the testing set and are shown to each par- ticipant. Four tone mapping results are displayed on the screen in random order. Users asked to pay atten- tion to the color, details, artifacts, and whether the local color is harmonious. As shown in Fig. 8, results achieve a better visual ranking compared to HDRNet and CLUT. Also, got 413 images ranked first and 576 images ranked second, accounting for 39.56% of the total, achieving results comparable to the ground truth (accounting for 39.64%).
700
GT
HDRNet
Ours
CLUT
300
200
100
Rank
Rank
Rank 3
Rank 4
Figure 8: Ranking results of the user study. Rank means the best visual quality.
Conclusion
This paper proposes learnable Differential Pyramid Representation Network (DPRNet), compre- hensive framework capable of recovering details and manipulating global and local tone mappings. We propose the LDP module capture multi-scale high-frequency components from input HDR images. To achieve global consistency and local contrast harmonization, we embed a GTP and an LTT module within DPRNet. Furthermore, we propose an IDE to gradually improve the image details by progressively refining the extracted high-frequee tiggffreeueecy components. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art methods, improving PSNR by 2.39 dB in the 4k HDR+ and 3.01 dB in the 4k HDRI Haven dataset, respectively, compared with the second-best best method. addition, our method has the best generalization ability in real-world video and image tone mapping.
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Appendix
Contents
1
Introduction
2 Related works
2.1 Learning-based Tone Mapping Methods
2.2 3D LUT-based Enhancement Methods
3
Methods
3.1
Motivation
3.2
Framework Overview
3.3
Learnable Differential Pyramid
3.4
Global-Local Collaborative Tone Mapping
3.5
Iterative Detail Enhancement
3.6
Loss Functions
4
Experiments
4.1
Experimental Settings
4.2
Comparison with State-of-the-ANe
4.3
Ablation Studies
4.4
10 Validation of Generalization
10
4.5
User Study
10
5
Conclusion
Loss Functions
B More Ablation Studies
B.1 Selection of the Number of Levels.
B.2 Ablation Study on Size of the 3D LUT's.
B.3 Selection of the Number of Grids.
B.4 Quantitative Comparisons High-Frequency Components
................................
B.5 More Generalization Comparisons
C DPRNet for HDR Video Tone Mapping
More Qualitative Results
Limitations and Discussion

---

In this appendix, we present related work and provide additional ablation studies, qualitative results, and discussion of limitations.
A Loss Functions
The proposed framework obtains faithful global and local enhancement by optimizing the reconstrue- tion loss, perceptual loss, and freeefrreeeney loss.
Reconstruction Loss. To maintain the accuracy the reconstructed image, we directly adopt pixel-wise Lite and Lysi loss the prediction T and the ground truth Y:
LR
Σ i=1
T Y + +I I Yll
(13)
Lusim MS-SSIM(T, Y),
(14)
where T; denotes the output of each layer of the network and Y, denotes the Gaussian pyramid of the ground truth.
High-Frequency Loss. To prompt the learnable differential pyramid module to extract effective information, we introduce high-frequert loss function. By calculating the L loss between the output high-frequency component and the high-frequency of ground truth:
LHF Σ H. 2=0
Y HF
11'
(15)
where Y!!!! denotes the high-frequency component of the GT obtained through the Laplacian pyramid.
Perceptual Loss. To make the learned DPRNet stable and robust, employ a perceptual loss function that assesses a solution concerning perceptually relevant characteristics (e.g., the structural contents and detailed textures):
Lp = 1 (T) (Y) 12.
(16)
where &o represents the 5th convolution (before maxpooling) layer within VGG19 network [49].
Output Loss. To summarize, the complete objective of our proposed model combined as follows:
Tittal a Lace + B Tussim + 7 LHF + Iop
(17)
where a, 8, y, and y are the corresponding weight coefficients.
B More Ablation Studies
B.1 Selection of the Number of Levels.
We validate the influence the number of pyramid levels n. As shown Tab. 7, the model achieves the best performance on all tested resolutions when 4. When larger number of levels (n 2 5) results in a significant decline in performance. This is because when n is larger and the number of downsamples is greater, the model fails to reconstruct the high frequencies efficiently, resulting in performance degradation.
B.2 Ablation Study on Size of the 3D LUTs.
To investigate the impact of 3D LUT (Look-Ur Up Table) size in the Local Tone Tuning (LTT) module, conduct ablation study by varying both the number of dimensions in the LUT (m) and the number of discrete levels (S.). Specifically, we examine two values for m (6 and 8) and two values for S, (9 and 17), which define the granularity and the size of the LUT used for local tonal adjustments.
The results are summarized in Tab.
In summary, the ablation study indicates that m and S 9 strikes the optimal balance between performance and computational efficiency, offering the best combination of image quality and structural similarity. Increasing S, m results in diminishing returns in performance, with significant increase in computational cost. Therefore, the choice of LUT size should be carefully considered to maintain efficiency while maximizing performance.

---

Table 7: Ablation study the number of levels.
Table 8: Ablation study on size of the Look- Up Table (3D LUT).
Number of Levels
Metrics
n=3
n=4
n=5
S.
PSNR
SSIM
#Params
PSNR 27.11
27.95
27.02
27.95
0.931
212K
SSIM
0,9179
0.9313
0.9109
8
9
27.92
0.926
214K
TMQI
0.8898
0.8883
0.8857
17
27.98
0.927
815K
LPIPS
0.0387
0.0335
0.0428
8
17
27.71
0.928
816K
AE 6.493
5.721
6.497
B.3 Selection of the Number of Grids.
To evaluate the impact of the number of grids (denoted as N) used in the Local Tone Tuning (LTT) module, conduct ablation study by varying the grid size. Specifically, examine four different grid sizes: N=2, N=4, N=6, and N=8. The results are summarized in Tab. 9, which reports performance across several metrics, including PSNR, SSIM, TMQI, LPIPS, color difference (AE), and the number of parameters in the model. As observed in Tab. 9, the N=4 strikes the optimal balance between model performance and computational efficiency. It achieves the best results across PSNR, SSIM, TMQL, LPIPS, and color fidelity, while maintaining a manageable model size. Larger grid sizes (N=6 and N=8) yield slightly worse performance, and smaller grids (N=2) not capture enough local detail for high-quality tone mapping.
Table 9: Ablation study on the number of grids in the L.T.
Grid Size N=4
Metrics
N=2
N=6
N=8
PSNR 25.77
27.95
27.03
27.41
SSIM
0.9146
0.9313
0.9203
0.9208
TMQI
0.8857
0.8883
0.8826
0.8855
LPIPS
0.0405
0.0335
0.0382
0.0373
AE 6.866
5.721
6.190
5.967
#Params
135K
212K
346K
530K
B.4 Quantitative Comparisons on High-F Begg-Fre Components
To further validate the effectiveness of Learnable Differential Pyramid (LDP) in high- high-frequency reconstruction, we conducted quantitative comparison with other methods using high- high-frequency components extracted via Laplacian decomposition. Specifically, measured the PSNR and SSIM of the extracted high-frequency (HF) maps, and additionally computed the Edge Preservation Index (EPI) using the Canny edge detector to assess the fidelity of fine structural details.
As shown in Table 10, our method achieves the highest PSNR-HF (25.92) and SSIM-HF (0.8637) among all methods, indicating superior reconstruction accuracy and structural consistency in high- frequency regions. While CODEFormer achieves the highest EPI (0.8333), our method closely follows with an EPI 0..8201, demonstrating strong edge retention capabilities. In contrast, traditional LUT-based methods (e.g., 3DLUT, SepLUT) perform significantly worse across all three metrics, highlighting the advantage of LDP in recovering fine-gre aen-ggrined textures. These results confirm that our LDP effectively preserves figh-frequency details and enhances fine structure reconstruction, outperforming both conventional and existing learning-basse tone mapping approaches.
B.5 More Generalization Comparisons
To verify the difference in generalization between the two datasets, we use the HDR+ data and the HDRI Haven dataset to train separately and test on the HDR Survey dataset [44]. We use the no-reference metric TMQI as the evaluation metric. Tab. shows that training on the HDRI Haven dataset is more suitable for the tone mapping task than HDR+, providing new benchmark for subsequent tone mapping tasks. Meanwhile, results in Tab. 11 show that our proposed DPRNet has stronger model generalization performance.
3

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Table 10: Quantitative Comparisons High-Prequency Components.
Methods
PSNR-HF
SSIM-HF
EPI
3DLUT
22.50
0.7436
0.6600
CLUT
22.79
0.7529
0.6745
CSRNet
22.71
0.7556
0.6704
HDRNet
24.02
0.8124
0.7569
SepLUT
22.22
0.7216
0.6491
LPTN
23.26
0.8079
0.7332
COTF
23.14
0.742
0.7241
CODEFormer
25.29
0,8592
0.8333
RECNet
24.10
0.8248
0.7626
Ours
25.92
0.8637
0.8201
Table 11: Differences in generalization between the HDR+ Dataset and the HDRI Haven dataset.
TMQI
Train Dataset
Test Dataset
CSRNet
LUT
CLUT
LPIN
CoTF
Ours
HDR+ datasel
HDR Survey
0.7754 0.8439
0.7847 0.8165
0.7638
0.7892
0.7959 0.8612
0.8157 0.9245
HDRI Haven dataset
HDR Survey
0.8140
0.8964
C DPRNet for HDR Video Tone Mapping
In this section, further demonstrate the effectiveness and model generalization performance of the proposed DPRNet for video tone mapping. We use the HDRI Haven image dataset for training and testing on the UVTM video dataset [50]. For display purposes. we have created an anonymous web page showing our results on the video dataset (DPRNet). As can be observed from the four sets of videos shown, our method generates videos with suitable contrast between light and dark and does not suffer from any unnatural textures. As shown in Fig. 10, our method exhibits perfect tone mapping results in different sophisticated scenes, proving the effectiveness of our proposed DPRNet and the strong model generalization ability.
D More Qualitative Results
We provide additional qualitative comparisons on the HDR Survey, UVTM video, HDRI Haven, and HDR+ datasets. Fig. 9 to Fig. 14 show 17 sets of qualitative comparisons. It is observed that DPRNet successfully produces outputs with finer details and sharper edges. Furthermore, with the proposed joint global and local tone mapping. DPRNet further improves the color saturation of the output results. can be observed that our proposed method achieves the best TMQI scores. For TMQI, our method outperforms the second-bes best method [31] by 0.053 the UVTM dataset. It demonstrates that our method is good revealing details and keeping temporal consistency. Fig. shows four sets of qualitative comparisons the HDR Survey dataset. From the examples, restore the fine details, leading to plausible results. Notably, we train on the HDRI Haven dataset and test the HDR Survey dataset and the UVTM video dataset. This proves that DPRNet has not only strong robustness but also strong generalization performance.
see that DPRNet can
E Limitations and Discussion
Our method does have limitations. For the MIT FiveK dataset, our method unable to obtain SOTA results, and we have performed some analyses on this. Firstly, FiveK was shot by DSLR camera, which was not processed by an advanced ISP pipeline and lacked raw denoising and YUV denoising, resulting in the dataset containing serious noise. These noises have great influence on our high-frequency extraction. Second, some reference images in the FiveK dataset suffer from overexposure or oversaturation, which poses challenge to the enhancement method, as mentioned by Zhang et al [4]. Thirdly, there are inconsistencies in the reference images adjusted by the same professional photographer, leading to differences between the training and test sets. As future work, we plan build a unified model tackle more visual enhancement tasks by integrating denoising and tone mapping.

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Imput
CSRNet
HDRNet
3D LUT
14
CLUT
CoTF
SepLUT
Ours
Imput
CSRNet
HDRNet
3D LUT
CLUT
COTF
SepLUT
Ours
A NAPI
Imput
CSRNet
HDRNet
3D LUT
AMARI
NATH
CLUT
CoTF
SepLUT
Ours
Input
CSRNet
HDRNet
3D LUT
CLUT
CoTF
SepLUT
Ours
Figure 9: Visual comparisons between DPRNet and the state-of-the-an methods, training on the HDR Haven dataset, and testing on the HDR Survey dataset (real-worl) dataset).

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Input
CSRNet
HDRNet
3D LUT
CLUT
CoTF
SepLUT
Ours
Imput
CSRNet
HDRNet
3D LUT
CLUT
CoTF
SEpLUT
Ours
Figure 10: Visual comparisons between our DPRNet and the state-of-the-ar methods, training on the HDR Haven dataset, and testing on the UVTM video dataset [21] (real-world dataset).
30
POWOR
ROBKO
POWER
70
Imput
HDRNet
CSRNet
3D LUT
CLUT
10
PONKR
ROBKO
DONT
TUNGS
LPTN
CoTF
SEpLUT
Ours
GT
30
20
Input
HDRNet
CSRNet
3D LUT
CLUT
LPTN
CoTF
StpLUT
Ours
GT
Figure 11: Visual comparisons between our DPRNet and the state-of-the-art methods on the HDR+ dataset (480p resolution).

---

Imput
HDRNØ
CSRN
CLUT
LPTN
CoTF
SepL.UT
Ours
Imput
HDRN
CSRN
3D
CLUT
LPIN
CoTF
SepLUT
Ours
CT
Imput
HDRNot
CSRNot
3D LUT
CLUT
CoTF
SepLUT
Our
Input
HDRN
CSRNet
CLUT
LPTN
C.TF
SEpLUT
Ours
GT
Input
HDRNat
CSRNet
3D LUT
CLUT
LPIN
COTF
StpLUT
Ours
Figure 12: Visual comparisons between our DPRNet and the state-of-the-art methods on the HDRI Haven dataset (480p resolution).

---

TH
Import
HDRN
CSRNet
3D
CLUT
LPTN
CofF
SepLUT
Input
HDRNet
CSRNet
3D LUT
CLUT
LPTN
CoTF
SepLUT
OUES
GT
Figure 13: Visual comparisons between our DPRNet and the state-of-the-art methods on the HDRI Haven dataset (4K resolution).
25
20
Input
HDRNet
CSRNet
LUT
CLUT
10
LPTN
CoTF
SEpLUT
Ours
GT
30
25
29
Input
HDRNet
CSRNet
LUT
CLUT
LPTN
COFF
SepLUT
Ours
GT
Figure 14: Visual comparisons between our DPRNet and the state-of-the art methods on the HDR+
dataset (4K resolution).


Reviews:
[{"review_id": "8akoa5d9hm", "reviewer": "Reviewer_nCUf", "summary": "This paper proposes DPRNet for tone mapping HDR images to LDR displays. It integrates global and local tone mapping into an end-to-end learnable framework using a global tone perception module for global adjustments, a local tone tuning module for patch-level refinements, a learnable differential pyramid for capturing multi-scale details, and an iterative detail enhancement module for high-resolution detail recovery.", "questions": "1. DPRNet reports lower FLOPs and parameter counts than LPTN, but longer runtime. Could you share insights on which components (e.g., iterative refinement or LUT tuning) dominate the inference time and whether they could be optimized?\n\n2. The model generalizes to video data, but does not explicitly model temporal consistency. Have you considered extensions that incorporate temporal constraints, such as recurrent mechanisms or temporal losses?\n\n3. Could the proposed modules be generalized to other HDR-related tasks such as exposure fusion or inverse tone mapping?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n- The proposed method achieves better performance compared to previous tone mapping methods while maintaining comparable computational complexity, suggesting its potential for practical use.\n- A comprehensive ablation study evaluates the contributions of each module and loss component, offering clear justification for the design decisions made in the network.\n\nWeakness\n- While the model demonstrates generalization to video datasets, experiment does not explicitly account for temporal consistency across frames. Incorporating temporal coherence could further enhance its applicability to HDR video tone mapping.\n- Despite its lower parameter count and computational complexity, DPRNet shows a relatively longer runtime than LPTN. Clarifying the underlying reasons could provide useful insights for future optimization.", "was_revised": true, "final_justification": "The authors have provided a thoughtful and detailed response. The clarifications and additional analyses sufficiently address the concerns I raised. Including these clarifications in the final version would improve the overall clarity of the paper. I maintain my original score.", "rebuttal": "**Response to Reviewer nCUf**\n\nWe sincerely thank Reviewer **nCUf** for their positive and encouraging feedback, as well as their insightful questions. We are delighted that the reviewer appreciated the performance of our method, the comprehensive ablation studies, and its potential for practical use. We provide detailed answers to the questions below.\n\n> **Q1:** DPRNet reports lower FLOPs and parameter counts than LPTN, but longer runtime. Could you share insights on which components (e.g., iterative refinement or LUT tuning) dominate the inference time and whether they could be optimized?\n\n**A1:** Thank you for this excellent and highly practical question. The discrepancy between theoretical metrics like FLOPs and actual runtime is a crucial point. To provide a clear answer, we profiled our model's components and found that the runtime is primarily influenced by memory access patterns and limited parallelism, factors not captured by FLOPs.\n\n**Table R1: Ablation studies of the runtime**\n\n| Configuration | Params (M) | GFLOPs | Relative Inference Time |\n|:--------------|:----------:|:------:|:-----------------------:|\n| Full DPRNet   |   0.212    |  6.9   |         1.000X          |\n| w/o LDP       |   0.180    |  2.5   |        0.793X  (-21%)        |\n| w/o GTP       |   0.176    |  6.8   |        0.998X  (-0.2%)       |\n| w/o LTT       |   0.102    |  6.8   |         0.639X  (-36%)        |\n| w/o IDE       |   0.179    |  4.4   |         0.734X  (-27%)        |\n\nOur analysis reveals three key insights directly linking our architectural choices to the runtime:\n\n1. **LTT (Overhead from multiple operators):** The LTT module is the most significant factor (-36% runtime). While its FLOPs are low, the patch-wise application of 16 distinct 3D LUTs involves significant operator overhead, which is not reflected in the FLOPs metric.\n\n2. **IDE (Sequential Dependency):** The IDE module's coarse-to-fine nature is inherently sequential, limiting GPU parallelism and adding 27% to the runtime. A network with a more parallel, feed-forward architecture can run faster even with higher FLOPs.\n\n3. **LDP (Memory-Bound Operations):** The LDP module adds 21% to the runtime. Its operations (e.g., full-resolution subtractions) are memory-bandwidth bound, meaning the bottleneck is data movement, not computation. FLOPs do not account for this memory access cost.\n\nFor optimization, we see clear paths forward. For LTT, LUT pruning or quantization can reduce complexity. For IDE and LDP, kernel fusion could minimize memory access latency. We will add this detailed analysis to our appendix.\n\n---\n\n> **Q2:** The model generalizes to video data, but does not explicitly model temporal consistency. Have you considered extensions that incorporate temporal constraints, such as recurrent mechanisms or temporal losses?\n\n**A2:** This is a fantastic question pointing to a valuable future direction. The reviewer is correct; our current work focuses on single-image processing, and **video results were generated frame-by-frame to show generalization.**\n\nWe agree that explicit temporal modeling is the key to producing flicker-free videos. As part of our future research plan, we have considered several promising extensions for which our modular design is well-suited:\n\n1. **Recurrent Mechanisms:** Integrating a ConvGRU to propagate temporal information between frames within our GTP module.\n\n2. **Temporal Consistency Loss:** Applying a loss on the difference between consecutive output frames ($T_{t+1}, T_t, T_{t-1}$) to encourage smoothness.\n\n3. **Optical Flow:** Using flow to warp previous frames or features, providing a stronger motion-aware consistency signal.\n\nWe believe these extensions will further enhance DPRNet’s applicability to dynamic HDR video content, and we look forward to incorporating them in an extended version.\n\n---\n\n> **Q3:** Could the proposed modules be generalized to other HDR-related tasks such as exposure fusion or inverse tone mapping?\n\n**A3:** Thank you for this excellent question about our method's broader significance. To demonstrate its generalizability, we have already evaluated DPRNet on the **inverse tone mapping (iTMO) task**. The results on three challenging benchmarks are shown below.\n\n**Table R2: Generalization to Inverse Tone Mapping**\n\n| Dataset             | Task                 | PSNR (dB) ↑ |   SSIM ↑  |\n|:--------------------|:---------------------|:---------:|:------:|\n| HDRPlus             | Inverse Tone Mapping |   31.94   | 0.9329 |\n| HDRI Haven          | Inverse Tone Mapping |   28.31   | 0.8953 |\n| HDRTVDM (2446c) | Inverse Tone Mapping |   33.40 | 0.9092 |\n| HDRTVDM (2446a) | Inverse Tone Mapping |  38.46 | 0.9536 |\n\nThese strong results confirm that our core components are highly effective for iTMO. Specifically, our LDP is crucial for hallucinating lost high-frequency details, while the GTP/LTT framework excels at learning the complex, non-linear expansion curves. We believe this demonstrates the high potential of our modules for a range of HDR-related tasks.\n\nOnce again, we are deeply thankful for your constructive feedback. Your suggestions have directly contributed to strengthening our work. Please do not hesitate to let us know if further clarification is needed."}, {"review_id": "x8RfhNsH75", "reviewer": "Reviewer_wHV5", "summary": "This paper proposes a novel tone mapping method, DPRNet, which aims to address two long-standing problems in tone mapping: the loss of high-frequency details and the disconnect between global and local tone adjustment operations. To this end, the authors introduce a learnable differential pyramid that enables content-aware and adaptive high-frequency information extraction and design a global-local collaborative tone mapping mechanism and an iterative detail enhancement module to maintain structural fidelity and perceptual consistency in reconstructed images. Also, the authors propose a new tone mapping dataset. The authors evaluated it on four tone mapping datasets and obtained state-of-the-art results that outperform existing methods on standard HDR datasets in terms of PSNR and visual quality.", "questions": "1. The paper notes the efficiency of the proposed methodology. The speed of inference is limited compared to methods such as LUT. It would be beneficial to ask the authors to further discuss the real-time deployment considerations.\n2. It is unknown how to ablate GTP and LTT. Just directly replacing them with a simple CNNs?\n3. The results on FiveK dataset show that noises have a great influence on high-frequency details, so would the authors provide more results or generalization analysis on the highly noised night scenarios in the wild?", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The authors' motivation is very reasonable and addresses highly practical and persistent problems in HDR tone mapping—namely, halo artifacts, texture loss, and global-local tone imbalance—with a unified and learnable framework. This is of high relevance to both academic and applied HDR imaging research.\n2. The learnable differential pyramid represents a substantive innovation over traditional handcrafted pyramids. Its content-aware design allows for effective and robust high-frequency extraction under diverse luminance and structural conditions.\n3. The proposed DPRNet is well-structured, with complementary modules for global tone perception and local tone refinement, followed by a coarse-to-fine restoration of details. The modular design is elegant and intuitive.\n4. The introduction of the HDRI Haven dataset is a significant contribution to the community. It supports reproducibility and may serve as a valuable benchmark for future tone mapping research.\n5. The results are compelling both quantitatively and visually. The inclusion of ablation studies, visualizations of the LDP, and perceptual comparisons strengthens the paper’s claims.\n\nWeaknesses:\n1. The contribution of the dataset is recognizable, but the authors should have provided more details of the dataset.\n2. In GTP, it is better to clarify the reason for pre-layer modulation within global tone.\n3. Some details remain unclear, such as the values of loss weights and the generation of $D_0$.\n4. I recommend making the code public for the reproducibility of this work.", "was_revised": true, "final_justification": "The authors’ detailed responses have adequately addressed my concerns. Some improvements in clarity are expected in the revision. Overall, I maintain my original score of \"Accept\".", "rebuttal": "**Response to Reviewer wHV5**\n\nWe sincerely thank Reviewer **wHV5** for the encouraging and constructive feedback. We are delighted that the reviewer recognized the value of our work and are grateful for the opportunity to provide further clarification on these important points.\n\n> **Q1:** The contribution of the dataset is recognizable, but the authors should have provided more details of the dataset.\n\n**A1:**  We thank the reviewer for this valuable suggestion. Our HDRI Haven dataset was built as follows:\n\n1. **Diverse Scene Collection:** We gathered a wide range of real-world scenes from [HDRI Haven](https://polyhaven.com/hdris), including outdoor, urban, studio, sunrise/sunset, and nightscapes.\n\n2. **Candidate Generation:** For each HDR image, we generated a pool of LDR candidates using professional software (Photomatix and Aurora HDR 2019) and classic TMOs to ensure methodological diversity.\n\n3. **Quality-Driven Selection:** First, we automatically filtered candidates using the TMQI and BTMQI metrics. Then, we performed expert manual selection, especially for challenging cases like highlights and night scenes, ensuring superior perceptual quality of the final ground truth.\n\nWe believe these details will clarify the value of our dataset. Furthermore, **we commit to publicly releasing the HDRI Haven dataset** upon acceptance to foster future research.\n\n---\n\n> **Q2:** In GTP, it is better to clarify the reason for pre-layer modulation within global tone.\n\n**A2:**  Thank you for this insightful question. The per-layer modulation in our GTP is a principled inductive bias designed to guide the network to learn a powerful and truly global tone transformation.\n\n1.  **Global Context Extraction (via GAP):**\nFirst, we use a condition network $C(\\bullet)$ with Global Average Pooling (GAP) to collapse the input image's spatial dimensions into a single global context vector: $\\mathbf{z} = \\text{GAP}(C(L_3))$. This vector acts as a condition of the entire scene's global properties (brightness, contrast, etc.).\n\n2. **Globally-Conditioned Transformation:** \nThis global vector $\\mathbf{z}$ is then used to generate a single set of modulation parameters ($\\gamma, \\beta$)  shared across all spatial locations. When applied to the convolutional layers in Eq. (4), this shared signal $(\\gamma, \\beta)$ uniformly modulates the layer responses, with all spatial locations receiving the same conditioning from $\\mathbf{z}$.\n\nIn summary, per-layer modulation guides a powerful universal function approximator (the CNN) to specifically and adaptively solve the global tone mapping problem, ensuring the learned transformation is both globally consistent and expressive beyond simple linear models.\n\n\n---\n\n> **Q3:** Some details remain unclear, such as the values of loss weights and the generation of D_0.\n\n**A3:** We thank the reviewer for pointing out these missing details. We will add them to the main paper to improve clarity and reproducibility.\n\n**Loss Weights:**\nThe weights, determined via ablation studies, are:\n\n$L_\\mathrm{total} = 0.8 \\cdot L_\\mathrm{Re} + 0.8 \\cdot L_\\mathrm{ssim} + 0.8 \\cdot L_\\mathrm{p} + 0.2 \\cdot L_\\mathrm{HF}$\n\nHere, $L_\\mathrm{Re}$ is the reconstruction loss, $L_\\mathrm{ssim}$ is the structural similarity loss, $L_\\mathrm{p}$ is the perceptual loss, and $L_\\mathrm{HF}$ is our proposed loss for supervising high-frequency detail learning. \n\n**Generation of $D_0$:**\nThe $D_0$ is the initial high-frequency detail map extracted by our LDP at level 0 (the original resolution). It is generated by a DoGLayer module which performs the following:\n\na) Three sequential $3\\times3$ convolutions are applied to the input image $X$, producing progressively smoother feature maps $\\lbrace G_1, G_2, G_3 \\rbrace$. These act as learnable Gaussian-like filters.\n\nb) High-frequency details are computed via element-wise subtractions: $(X - G_1), (G_1 - G_2), (G_2 - G_3)$.\n\nc) These detail maps are concatenated to form the final multi-level high-frequency representation, $D_0$.\n\nThis process is a learnable version of a Difference-of-Gaussian pyramid, making it more adaptive to image content than fixed filters. We will add a formal algorithmic description to the paper.\n\n---\n\n> **Q4:** I recommend making the code public for the reproducibility of this work.\n\n**A4:** We completely agree and thank the reviewer for this recommendation. Upon paper acceptance, we will release full training and evaluation code. \n\n---\n\n> **Q5:** The paper notes the efficiency of the proposed methodology. The speed of inference is limited compared to methods such as LUT. It would be beneficial to ask the authors to further discuss the real-time deployment considerations.\n\n**A5:** We thank the reviewer for this important practical point. Our primary design goal was to achieve a new state-of-the-art in visual quality, which required a more expressive architecture than simple LUTs. This was a conscious trade-off: accepting a moderate computational cost for a significant leap in quality, especially for high-end applications (e.g., professional photography, film production).\n\nFor broader real-time applications where speed is critical, we see several clear paths for optimization:\n\n1. **Knowledge Distillation:** Using our powerful DPRNet as a teacher to train an extremely fast student network (e.g., a lightweight CNN or even a 3D LUT), achieving a new, superior balance of speed and quality.\n\n2. **Model Compression:** Applying established techniques like network pruning and quantization.\n\n3. **Architectural Simplification:** Training a lighter version of DPRNet (e.g., with fewer layers or iterations) for speed-critical scenarios.\n\nThank you for prompting us to clarify this crucial aspect of our work.\n\n---\n\n> **Q6:** It is unknown how to ablate GTP and LTT. Just directly replacing them with a simple CNNs?\n\n**A6:** Thank you for this clarification question. Our ablation study employed a \"module drop\" protocol. Instead of replacing a module with a different CNN, we removed it entirely from the network. This method cleanly isolates each component's true contribution without introducing confounding variables from a replacement network. We will clarify this protocol in the experimental section.\n\n---\n\n> **Q7:** The results on FiveK dataset show that noises have a great influence on high-frequency details, so would the authors provide more results or generalization analysis on the highly noised night scenarios in the wild?\n\n**A7:** This is a critical point about robustness. To demonstrate this, we have provided extensive results on challenging \"in-the-wild\" night scenes on our anonymous [demo page](https://xxxxxxdprnet.github.io/DPRNet/). We will add a dedicated analysis of these challenging cases to the supplementary material.\n\nOnce again, we are deeply thankful for your constructive and detailed feedback. Your suggestions have been invaluable in helping us strengthen the paper. We hope our responses and planned revisions have fully addressed your concerns."}, {"review_id": "ofJtu1Rqpp", "reviewer": "Reviewer_iFjE", "summary": "The paper presents an end-to-end trainable framework for tone mapping based on a Learnable Differential Pyramid (LDP), which generalizes classical Difference-of-Gaussian pyramids through adaptive filtering and differencing to enable robust high-frequency extraction. To ensure tonal consistency, the framework introduces a Global–Local Tone Mapping strategy, consisting of: (1) a Global Tone Perception (GTP) module that processes downsampled inputs to enable scene-aware luminance adjustment, and (2) a Local Tone Tuning (LTT) module that refines patch-wise tonal characteristics using learned 3D LUTs. Finally, an Iterative Detail Enhancement (IDE) module progressively integrates high-frequency details in a coarse-to-fine manner, starting from the LTT output and incorporating information from the LDP to enhance fine structures and local contrast.", "questions": "The authors should thoroughly address all issues raised above concerning the clarity of the paper.", "limitations": "The authors mention that their method does not perform well on the MIT FiveK dataset but do not report quantitative results or comparisons to other methods on this dataset. Including these results, even as a negative case, would improve transparency and help the community understand the limitations of the proposed approach.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Demonstrates good empirical performance across various benchmarks.\n- Introduces a new HDR dataset (HDRI Haven dataset), which can be valuable for the community.\n\nWeaknesses:\n\nMy main concern is a significant lack of clarity in the technical description, which impacts the paper’s readability and reproducibility:\n- The pipeline for the Learnable Differential Pyramid is described verbally, but it is unclear what specific mathematical operations are taking place, making reproducibility difficult.\n- In line 142, the paper states that the globally tone-mapped image is the output F^{(0)}, but in formula (4), F^{(l)} is used for the output at layer l. This inconsistency needs clarification.\n- It is unclear whether the Global Tone Perception (GTP) module indeed produces a globally tone-mapped image. Based on formula (4), it appears to perform only an MLP computation, raising the question of how it enforces global tone mapping properties.\n- The ablation study in the appendix discusses the number of LUT dimensions (m) and discrete levels (S_t), but these symbols are never introduced in the main paper’s description of the 3D LUTs.\n- It is not clear whether the basis LUTs are also learned during training.\n- The high-frequency loss function L_{HF} is only described in the appendix\n- The paper lacks a Related Work section in the main text, which makes it difficult to position the proposed method relative to prior state-of-the-art approaches. While an extended related work can remain in the appendix, a concise section in the main paper is essential for readers to more precisely understand how the proposed method differs with respect to prior approaches in this area.\n\nCollectively, these issues hinder the readability and clarity of the paper and would require a major revision to improve the technical presentation.", "was_revised": true, "final_justification": "I appreciate the author’s response; however, the explanation remains unclear and does not appear consistent with the description in the paper, despite my having asked twice for clarification. For example, in their most recent reply they state that “The Modulated Convolutional Core then uses this single global vector z to generate modulation parameters (γ, β).” Yet, according to equation (4) in the paper, the modulated convolutional core seems to instead take (γ, β) as input in order to produce the globally tone-mapped image as output.\n\nIn my view, the paper requires a thorough major revision to present a clearer and more precise technical description of the proposed approach. For this reason, I will maintain my original rating.", "rebuttal": "**Response to Reviewer iFjE**\n\nWe sincerely thank Reviewer **iFjE** for their meticulous and thoughtful review. We are very encouraged by the positive assessment of our work's potential and are grateful for this opportunity to address the critical issues of clarity you have raised. We apologize that our initial presentation was not clear enough and have a concrete plan to revise it.\n\n> **Q1:** The pipeline for the Learnable Differential Pyramid is described verbally, but it is unclear what specific mathematical operations are taking place, making reproducibility difficult.\n\n**A1:** We apologize for the lack of mathematical clarity and thank you for this critical feedback. We will replace the verbal description with a formal algorithmic description to ensure full reproducibility. We also commit to releasing our full code upon acceptance.\n\nThe core operations of our LDP for each pyramid level $i \\in \\lbrace 0, 1, 2 \\rbrace$ are:\n\n1. **Multi-Scale Differencing:** The *DoGLayer* computes a set of Difference-of-Gaussian-like features. Given an input feature $F_i$, it generates three progressively smoother versions $\\lbrace G_{i,1}, G_{i,2}, G_{i,3} \\rbrace$ via sequential convolutions. The raw detail feature $D_i$ is formed by:\n$D_i = \\text{concat}(F_i - G_{i,1}, G_{i,1} - G_{i,2}, G_{i,2} - G_{i,3})$\n\n2. **Downsampling:** The input for the next level is created by pooling the smoothest feature:\n$F_{i+1} = \\text{MaxPool}(G_{i,3})$\n\n3. **Detail Refinement:** The final detail map for each level is generated by refining the raw detail feature $D_i$ by a sub-network *hfmaplaye$r_i$*:\n$H_i = \\text{hfmaplayer}_i(D_i)$\n\nThis iterative process yields the detail pyramid $\\lbrace H_0, H_1, H_2 \\rbrace$. This formal description will be added to the paper to ensure our method is unambiguous and reproducible.\n\n---\n\n> **Q2:** In line 142, the paper states that the globally tone-mapped image is the output F^{(0)}, but in formula (4), F^{(l)} is used for the output at layer l. This inconsistency needs clarification. \n\n**A2:** We sincerely apologize for this confusing typo. You are correct, and we will fix this notational inconsistency in the revised paper. The correct formulation is:\n\n1. **Input:** The input to the GTP's modulated core is $F^{(0)} = L_3$.\n\n2. **Layer-wise Computation:** For layer $l \\in \\lbrace 1, 2, 3 \\rbrace$, The formula (4) is expressed as:\n$F^{(l)} = \\text{ReLU}\\left(\\text{W}^{(l)}*F^{(l-1)} \\cdot \\gamma^{(l)} + \\beta^{(l)} + F^{(l-1)}\\right)$\n\n3. **Final Output:** The globally tone-mapped image is the output of the final layer, $L_3^G = F^{(3)}$.\n\nWe will update all text and equations to reflect this correction. Thank you for helping us improve the paper's precision.\n\n---\n\n> **Q3:** It is unclear whether the Global Tone Perception (GTP) module indeed produces a globally tone-mapped image. Based on formula (4), it appears to perform only an MLP computation, raising the question of how it enforces global tone mapping properties.\n\n**A3:** This is an excellent question that gets to the heart of our GTP design. The key to its \"global\" nature lies not in the convolutional formula itself, but in how the modulation parameters ($\\gamma, \\beta$) are generated. This is a two-step process:\n\n1.  **Global Context Extraction (via GAP):**\nFirst,  we use a condition network $C(\\bullet)$ with Global Average Pooling (GAP) to collapse the input image's spatial dimensions into a single global context vector: $\\mathbf{z} = \\text{GAP}(C(L_3))$. This vector acts as a condition of the entire scene's global properties (brightness, contrast, etc.).\n\n2. **Globally-Conditioned Transformation:** \nThis global vector $\\mathbf{z}$ is then used to generate a single set of modulation parameters ($\\gamma, \\beta$)  shared across all spatial locations. When applied to the convolutional layers in Eq. (4), this shared signal $(\\gamma, \\beta)$ uniformly modulates the layer responses, with all spatial locations receiving the same conditioning from $\\mathbf{z}$.\n\nIn this sense, we emphasize that this module is a globally conditioned network, named the Global Tone Perception (GTP) module. We will further highlight the critical role of global conditioning in our revised manuscript to better clarify this mechanism.\n\n---\n\n> **Q4:** The ablation study in the appendix discusses the number of LUT dimensions (m) and discrete levels (S_t), but these symbols are never introduced in the main paper’s description of the 3D LUTs.\n\n**A4:**  We sincerely apologize for this disconnect between the main paper and the appendix. You are correct that these symbols should have been introduced. These are internal hyperparameters of our 3D LUTs:\n\n1. $S_t$ denotes the LUT's grid density, determining the grid size of 3D LUTs as $S_t \\times S_t \\times S_t$.\n\n2. $m$ denotes the number of basis LUTs used in our efficient low-rank parameterization.\n\nIn the revised paper, we will briefly introduce $S_t$ and $m$ in the main LTT section and explicitly refer to the appendix, where the ablation study justifies our choice of $S_t=9$ and $m=6$. This will properly connect the main text with the supplementary material.\n\n---\n\n> **Q5:** It is not clear whether the basis LUTs are also learned during training.\n\n**A5:** \nThank you for this clarifying question. Yes, the basis LUTs are also learnable. They are implemented as the weights of an nn.Linear layer and are jointly optimized with the rest of the network during the end-to-end training. This allows the model to learn the most effective basis transformations directly from the data. We will make this explicit in the LTT section.\n\n---\n\n> **Q6:** The high-frequency loss function L_{HF} is only described in the appendix\n\n**A6:** We thank the reviewer for the careful review. Since high-frequency loss $L_{HF}$ is critical for supervising our novel LDP module, it should be defined in the main manuscript. In the revised version, we will move the full mathematical definition of our $L_{HF}$ from the appendix to the \"Loss Functions\" section in the main manuscript to make our training mechanism clear and self-contained.\n\n---\n\n> **Q7:** The paper lacks a Related Work section in the main text, which makes it difficult to position the proposed method relative to prior state-of-the-art approaches. \n\n**A7:** We completely agree with the reviewer and thank you for this crucial suggestion regarding the paper's structural integrity.\n\nDue to strict page limits, we initially attempted to integrate the discussion of related work into the Introduction and place a more extensive review in the appendix. We now fully recognize that this tends to weaken the context of our work and does not meet the standard for a top-tier conference paper.\n\nWe promise that in the final revised manuscript, we will put “Related Work” in the main text to help readers understand the context of our work.\n\n---\n\n> **Limitations:** The authors mention that their method does not perform well on the MIT FiveK dataset but do not report quantitative results or comparisons to other methods on this dataset. Including these results, even as a negative case, would improve transparency and help the community understand the limitations of the proposed approach.\n\n**A8:** We thank the reviewer for this excellent suggestion to improve the paper's transparency. We agree that providing these results is crucial for understanding our method's applicability.\n\nAs requested, we have conducted the quantitative comparison on the MIT-Adobe FiveK dataset:\n\n**Table R3: Quantitative comparison on MIT-Adobe FiveK dataset.**\n\n| Method | PSNR (dB) ↑ |   SSIM ↑  |\n|:-------------------------|:-----------:|:---------:|\n| UPE   |    21.82    |   0.839   |\n|HDRNet   |    23.31    |   0.881   |\n| DeepLPF   |    24.97    |   0.897   |\n| sLUT     |  24.67  | 0.896 |\n| CLUT     |  24.94  | 0.898 |\n| Ours     |  23.47  | 0.879 |\n\nThese results confirm that our method does not achieve SOTA performance on this specific dataset. As discussed in our limitations, the FiveK dataset is not suitable for tone mapping tasks.\n\n1. FiveK was captured with a DSLR camera and has not been processed by an advanced ISP pipeline. It lacks raw denoising and YUV denoising, resulting in a dataset with severe noise; this noise has a significant impact on high-frequency extraction. \n\n2. Some reference images in the FiveK dataset suffer from overexposure or oversaturation, which poses a challenge to the tone mapping method, as mentioned by LLFLUT. \n\n3. There is an inconsistency in the reference images adjusted by the same professional photographer, resulting in differences between the training and test sets.\n\nThank you again for your suggestion. Your suggestions have been invaluable in helping us strengthen the paper. We hope our responses and planned revisions have fully addressed your concerns. Please do not hesitate to let us know if further clarification is needed."}, {"review_id": "EkmXdZ75Mm", "reviewer": "Reviewer_aHBS", "summary": "This paper proposes an end-to-end framework called Differential Pyramid Representation Network (DPRNet) for high-fidelity HDR image tone mapping. DPRNet incorporates a Learnable Differential Pyramid (LDP), along with Global Tone Perception (GTP) and Local Tone Tuning (LTT) modules, as well as Iterative Detail Enhancement (IDE). To support training and benchmarking, the authors also introduce a new HDR tone mapping dataset called HDRI Haven.", "questions": "- In addition to Laplacian pyramids and Gaussian pyramids, there are other types of learnable designs for frequency band decomposition. For example, OctConv separates high-frequency and low-frequency information. what are the advantages of the proposed Learnable Differential Pyramid compared to OctConv?\n\n- The LUT-based method adjusts the tone of each image block individually after dividing the image into patches, which differs from the holistic processing strategy adopted by most existing methods. However, such locally independent adjustments may introduce tone inconsistencies between regions. Furthermore, within each block, the adjustment process still appears to be globally oriented, especially in high-resolution image scenarios.\n\n- Regarding the GT (Ground Truth) generation process of the constructed dataset, the paper mentions the integration of multiple software tools and color grading algorithms, combined with a selection strategy for the final determination. What are the specific criteria behind this selection strategy? How is the objectivity and high quality of the generated GT quantified or ensured?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n- This paper proposes a unified framework that simultaneously addresses global tone adjustment, local tone perception, and detail enhancement. A differential pyramid is designed to effectively extract high-frequency features.\n\n- The paper includes extensive comparative experiments and ablation studies. The proposed method demonstrates significant advantages in image enhancement, outperforming existing approaches in both subjective perception and objective metrics.\n\n- Additionally, the authors construct a new dataset to support the training and evaluation of tone mapping tasks, further advancing research in this field.\n\nWeaknesses\n-  there are numerous methods that combine global adjustment with local/detail enhancement for image processing, so the overall framework of this paper is relatively common.", "was_revised": true, "final_justification": "The author's rebuttal addressed my concerns, and my final rating is Accept.", "rebuttal": "**Response to Reviewer aHBS**\n\nWe sincerely thank Reviewer **aHBS** for the insightful and constructive feedback. We are grateful for the opportunity to provide further clarification on these important technical points.\n\n> **Q1:** In addition to Laplacian pyramids and Gaussian pyramids, there are other types of learnable designs for frequency band decomposition. For example, OctConv separates high-frequency and low-frequency information. What are the advantages of the proposed Learnable Differential Pyramid compared to OctConv?\n\n**A1:** This is an excellent question. While both LDP and OctConv are learnable, they differ fundamentally, making LDP better suited for high-fidelity tone mapping.\n\n1. **Conceptual Difference:** OctConv is a general-purpose operator for efficient single-scale convolution. In contrast, our LDP is a specialized multi-scale architecture designed to explicitly extract a hierarchical pyramid of details ($H_i$), directly mimicking and improving upon classical image pyramids for detail-sensitive tasks.\n\n2. **Empirical Evidence:** We conducted an ablation study comparing LDP to an OctConv-based decomposition. The results on our HDRplus dataset validate our design:\n\n**Table R4: Ablation study on the detail extraction module**\n\n| Detail Extraction Method | PSNR (dB) ↑ |   SSIM ↑  |\n|:-------------------------|:-----------:|:---------:|\n| Gaussian Pyramid (GP)    |    26.04    |   0.912   |\n| Laplacian Pyramid (LP)   |    25.90    |   0.912   |\n| OctConv Decomposition    |    27.31    |   0.916   |\n| **LDP (Ours)**           |  **27.95**  | **0.931** |\n\nThe data shows that while OctConv is effective, our LDP provides a further substantial gain of **0.64 dB** in PSNR. This confirms that for high-fidelity tone mapping, an explicit, multi-scale detail extraction architecture like LDP is superior. We will add this important comparison to the appendix.\n\n---\n\n> **Q2:** The LUT-based method adjusts the tone of each image block individually after dividing the image into patches, which differs from the holistic processing strategy adopted by most existing methods. However, such locally independent adjustments may introduce tone inconsistencies between regions. Furthermore, within each block, the adjustment process still appears to be globally oriented, especially in high-resolution image scenarios.\n\n**A2:** This is a crucial point, and we designed our LTT module specifically to prevent such inconsistencies. The key is that our patch-wise adjustments are not independent. Our approach ensures consistency through three mechanisms:\n\n1. **Context-Aware LUT Generation:** We do not learn a separate LUT for each patch in isolation. Instead, we first extract features from the entire downsampled image. The LUT for each patch is then generated from a local descriptor that inherently contains information about its surrounding regions. This ensures a smooth transition between the LUTs of adjacent patches.\n\n2. **Explicit Boundary Blending:** As a second safeguard, we apply bilinear blending at the patch boundaries during reassembly, guaranteeing a visually seamless final output.\n\n3. **Synergy with GTP:** The LTT operates on the output of our GTP module, which has already performed a preliminary global tone alignment. LTT's task is not to perform tone mapping from scratch, but to apply fine-grained local tuning to the GTP's output. This division of labor allows LTT to focus on resolving regional contrast issues without the risk of destroying overall tonal harmony.\n\nThis \"analyze globally to inform local adjustments\" strategy allows our LTT to be both locally adaptive and globally consistent, avoiding the artifacts common in other local methods.\n\nIn summary, our LTT module does not perform isolated patch processing. By analyzing global context to predict locally adaptive LUTs and supplementing this with boundary blending, it achieves differentiated, fine-grained adjustments for diverse image regions (e.g., highlights, shadows) while ensuring seamless continuity across the entire image.\n\n---\n\n> **Q3:** Regarding the GT (Ground Truth) generation process of the constructed dataset, the paper mentions the integration of multiple software tools and color grading algorithms, combined with a selection strategy for the final determination. What are the specific criteria behind this selection strategy? How is the objectivity and high quality of the generated GT quantified or ensured?\n\n**A3:** Thank you for this important question about our dataset's integrity. Our GT generation follows a rigorous, quality-driven pipeline to ensure both objectivity and high perceptual quality:\n\n1. **Diverse Scene Collection:** We gathered a wide range of real-world scenes from HDRI Haven, including outdoor, urban, studio, sunrise/sunset, and nightscapes.\n\n2. **Candidate Generation:** For each HDR image, we generated a pool of LDR candidates using professional software (Photomatix and Aurora HDR 2019) and classic TMOs to ensure methodological diversity.\n\n3. **Quality-Driven Selection:** First, we automatically filtered candidates using the TMQI and BTMQI metrics. Then, we performed expert manual selection, especially for challenging cases like highlights and night scenes, ensuring superior perceptual quality of the final ground truth.\n\nThis hybrid pipeline ensures our GT is both quantitatively strong and visually pleasing. We commit to publicly releasing the HDRI Haven dataset upon acceptance.\n\nYour suggestions have been invaluable in helping us strengthen the paper. We hope our responses and planned revisions have fully addressed your concerns."}]

Author rebuttal:

Meta-review: This paper proposes an end-to-end framework, Differential Pyramid Representation Network (DPRNet), for high-fidelity HDR image tone mapping. DPRNet incorporates a Learnable Differential Pyramid (LDP), along with Global Tone Perception (GTP) and Local Tone Tuning (LTT) modules, as well as Iterative Detail Enhancement (IDE). Experiments on four tone mapping datasets, including the new one proposed in the paper, show that the proposed method outperforms existing methods on standard HDR datasets in terms of PSNR and visual quality.

The authors valued the well-structured method for tone-mapping, as well as a new dataset called HDR Haven. The method is well-motivated in terms of tone mapping, such as well-known issues like halo artifacts and global-local tone imbalance.

The paper would be highly valuable for the community, considering both the methodological and strong dataset contributions.
Decision: Accept (poster)