Learning Gain Map for Inverse Tone Mapping
Authors: Yinuo Liao
Yuanshen Guan
Ruikang Xu
Jiacheng Li
Shida Sun
Zhiwei Xiong
Venue: ICLR 2025 Poster (2025)
Keywords: Computational Photography
Inverse Tone Mapping
Gain Map
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: For a more compatible and consistent high dynamic range (HDR) viewing experience, a new image format with a double-layer structure has been developed recently, which incorporates an auxiliary Gain Map (GM) within a standard dynamic range (SDR) image for adaptive HDR display. This new format motivates us to introduce a new task termed Gain Map-based Inverse Tone Mapping (GM-ITM), which focuses on learning the corresponding GM of an SDR image instead of directly estimating its HDR counterpart, thereby enabling a more effective up-conversion by leveraging the advantages of GM. The main challenge in this task, however, is to accurately estimate regional intensity variation with the fluctuating peak value. To this end, we propose a dual-branch network named GMNet, consisting of a Local Contrast Restoration (LCR) branch and a Global Luminance Estimation (GLE) branch to capture pixel-wise and image-wise information for GM estimation. Moreover, to facilitate the future research of the GM-ITM task, we build both synthetic and real-world datasets for comprehensive evaluations: synthetic SDR-GM pairs are generated from existing HDR resources, and real-world SDR-GM pairs are captured by mobile devices. Extensive experiments on these datasets demonstrate the superiority of our proposed GMNet over existing HDR-related methods both quantitatively and qualitatively. The codes and datasets are available at https://github.com/qtlark/GMNet.

Published as a conference paper at ICLR 2025
LEARNING GAIN MAP FOR INVERSE TONE MAPPING
Yinuo Liao, Yuanshen Guan, Ruikang Xu, Jiacheng Li, Shida Sun, Zhiwei Xiong* University of Science and Technology of China, Hefei, China {yinuoliao, ysguan xurk, jolee, sdsun) | @maalml............a ustc. edu.eeduu.c.m en, zwxiongeuste. edu.
ABSTRACT
For a more compatible and consistent high dynamic range (HDR) viewing ex- perience, new image format with double-layer structure has been developed recently, which incorporates an auxiliary Gain Map (GM) within a standard dy- namic range (SDR) image for adaptive HDR display. This new format motivates us to introduce a new task termed Gain Map-based Inverse Tone Mapping (GM- ITM), which focuses on learning the corresponding GM of SDR image instead of directly estimating its HDR counterpart, thereby enabling more effective up- conversion by leveraging the advantages of GM. The main challenge in this task. however, is to accurately estimate regional intensity variation with the fluctuating peak value. To this end, we propose a dual-branch network named GMNet, con- sisting of a Local Contrast Restoration (LCR) branch and Global Luminance Es- timation (GLE) branch to capture pixel-wise and image-wise information for GM estimation. Moreover, to facilitate the future research of the GM-ITM task, we build both synthetic and real-world datasets for comprehensive evaluations: syn- thetic SDR-GM pairs generated from existing HDR resources, and real-world SDR-GM pairs are captured by mobile devices. Extensive experiments on these datasets demonstrate the superiority of our proposed GMNet over existing HDR- related methods both quantitatively and qualitatively. The codes and datasets are available httpss//gihhub.co//qlakkcCCc
INTRODUCTION
High dynamic range (HDR) images are widely employed in the media and film industries, offering realistic visual experience with vivid details. To enable more compatible and consistent HDR image display, a new image format with a double-layer structure has been developed recently (Apple, 2021; Adobe, 2024; Google, 2024; ISO, 2024). This format stores a standard dynamic range (SDR) image and an auxiliary Gain Map (GM), enabling HDR image adaptation across various devices. Specifically, it allows the SDR image be directly displayed on legacy devices, while applying the GM to the SDR image for HDR display on modern devices as shown in Fig. 1 (a).
Motivated by this novel double-layer format, we introduce new Inverse Tone Mapping (ITM) task to up-convert existing SDR images to their HDR version, which is termed Gain Map- based Inverse Tone Mapping (GM-ITM). It targets GM estimation instead of direct HDR image prediction, fully exploiting the following advantages of the GM: (1) Simplified construction: The GM stores pixel- level dynamic range information in a compact 8-bit depth image. with a resolution typically reduced to 1/4 or 1/16 of the original SDR image. (2) Balanced distribution: The GM exhibits a more balanced pixel-value distribution compared with the HDR image, as shown in Fig. (b). (3) Detail preservation: As shown in the top row of Fig. 1 (c), logarithm-encoded GM alleviates the over- compression problem, preserving more contrast and texture details in highlight regions.
In addition to leveraging the inherent advantages of the GM, the characteristics of the GM-ITM further enhance its superiority up-conversion compared to previous learning- based relatel tasks (Yao et al., 2023; Huang et al., 2023; Li et al., 2022; Liu et al., 2023). First, GM-ITM shifts the target from the HDR image to the intermediary GM, simplifying the learning process since pre- dicting the transformation from input to output is simpler than directly predicting the output (Gharbi
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Formilation
SEHOR
Lose O leps - lance
CM
(8) SDR-GM HDR process
(b) Histograms
Visual Comparison
Figure 1: (a) Schematic of the SDR-GM to HDR process and simplified formulation of Eq. 3. The GM records pixel-wise dynamic range information of the corresponding SDR image and can be used to restore HDR through element-wise multiplication. (b) Histograms of SDR, GM, and HDR, with logarithmic statistics computed across 100 images. The GM illustrates more balanced distribution compared to the long-tailed distribution of the HDR image, making it more tractable learning target. (c) Visual comparison of methods (Chen et al., 2021a; Xu et al., 2022) for different HDR- related tasks. The top demonstrates the supervision targets, while the rows below show the estimation results and the error maps, in which our method achieves the best visual experience.
al., 2017). Second, GM-ITM is implemented during the display process, enabling more flexi- ble up-conversion compared to previous tasks that aim at irradiance estimation of the scenes (Khan et al., 2019; Kim et al., 2021) or up-conversion of the images (Cheng et al., 2022; Liu et al., 2024).
While the GM brings benefits to the ITM process, it also presents challenges due to its unique char- acteristics, particularly in estimating regional intensity variation with the fluctuating peak value. To address this problem, propose GMNet, a network with two branches fitting the locality and glob- ality of the GM respectively, in which the Local Contrast Restoration (LCR) branch aims at recover- ing pixel-wise spatial information, and the Global Luminance Estimation (GLE) branch focuses predicting image-wiss wiis luminance information. Additionally, considering the intrinsic relationship between the locality and globality of the GM, introduce squeeze module for global guidance traction, and propose spatial-aware and channel-wiss modulation modules to align the LCR branch with the GLE branch, ensuring precise and consistent up-conversion across both aspects. Based on the above designs. GMNet not only overcomes the difficulties in learning GM. but also effectively utilizes the intrinsic connections behind these difficulties. thus achieving superior performance.
Furthermore, we build synthetic and real-world datasets to facilitate future research on the GM-ITM task. Specifically, the synthetic dataset consists of SDR-GM pairs derived from HDRTV -tttnddt videos, while the real-world dataset comprises high-resolution SDR-GH pairs captured by mobile devices. The datasets demonstrate extensive diversity, covering both daytime and nighttime scenes, as well as indoor and outdoor settings. The quantitative and qualitative experiments on these datasets indicate that our method significantly outperforms the existing HDR- related methods.
Contributions of this paper can be summarized as follows:
Motivated by new double-layer HDR image format, we introduce a new task named GM-ITM to achieve more effective up-conversion.
We propose a dual-branch network tailored for GM-ITM, exhibiting superior performance in both qualitative and quantitative evaluations to existing solutions.
To facilitate further research along this line, we build both synthetic and real-world datasets con- sisting of high-resolution and diverse SDR-GM pairs.
RELATED WORK
Learning-based HDR-related tasks have been long-standing research topic. However, many of them are similar name but differ in connotation, as shown in Tab. 1. In this section, we will distinguish the scopes of these tasks and introduce related studies.
Iingle-Image HDR reconstruction (SI-HDR). Due to the limited dynamic range of camera sen- sors, image quality degrades in extremely bright dark scenes, motivating SI-HDD methods to
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Input
Output
Task
Type LDR
OETF
Gamut Specific
Type HDR
OETF
Gamut
SI-HDR
Linear
Linear
Specific
HDR-style
LDR
Gamma
BT.709
SDR
Gamma
BT.7009
enhancement
SDR-to-HDRTV
SDR
Gamma
BT.709
HDR
PQ/HLG
BT.2020
GM
Logarithm*
N/A
GM-ITM
SDR
Gamma
P3
HDR
Linear
P3
Table I: Comparison of the leamiig-based HDR-related tasks. The table lists typical values, yet there may be alternatives in practice. The LDR and SDR essentially represent low dynamic range content, but the LDR image is derived from the camera ISP, while the SDR image degraded from the HDR content (Chen et al., 2021b). The color gamut of SI-HDR is device-specific, while other tasks follow the definition of BT.709 (TTU-R, 2015b), 2T,0020 (ITU-R, 2015a) and P3 (DCI, 2005; SMPTE, 2010), and the gamut is not applicable (N/A) for the GM. *The opto-electronic transfer function (OETF) of GM being undefined, we adopt the logarithm encoding function as a substitute.
restore HDR irradiance from the LDR source. The SI-HDD methods be broadly divided into two branches (Wang & Yoon, 2021), one of which is the direct mapping approach. Eilertsen et al. (2017) utilize an end-to-end CNN to recover details in over-exposed regions. HDRUNet (Chen et al., 2021a) uses spatially dynamic encoder-decoder network to learn the LDR-1o-HDR map- ping. KUNet (Wang et al., 2022) introduces a wwwwlldge-inspieee block to capture global infor- mation for HDR reconstruction. DCDR-UNG (Kim et al., 2024) introduces the deformable block to better restore lost details the over-exposed region regardless of the size. The other branch stack-based approach. Endo et al. (2017) propose the first deep-leerning--aseee approach that recon- structs the HDR image by merging estimated bracketed LDR images. Lee et al. (2018a) generate HDR images from multi-exposure stack using conditional generative adversarial network. Zhang et al. (2023) estimate two exposures to reconstruct HDR radiance from a single image. In general, SI-HDR methods focus on reconstructing missing details and recovering scene irradiance.
HDR-style enhancement. For a better visual experience within a limited dynamic range, HDR- style enhancement methods upgrade LDR images to SDR images in HDR-like view to improve perceptual quality. In previous studies, HDRNet (Gharbi et al., 2017) predicts locally affine model coefficients in bilateral space, achieving pleasing results the HDR plus dataset (Hasinoff al., 2016). Zheng et al. (2021) propose a dual-path network that reconstructs high-quality content and chromatic features using guided bilateral up-sampling. These enhancement methods aim to improve the visual quality of LDR images, but without dynamic range broadened in fact.
HDR- up-conversion. The popularity of HDR monitors calls for SDR-to-HDRTV up- conversion methods to broaden the dynamic range and the color gamut, upgrading the existing SDR source to the HDRTV version. Kim & Kim (2019) first investigate this by jointly learning super- resolution and inverse tone- mapping.. FMNet (Xu et al., 2022) introduces frequency- mod- ulation block to reduce structural distortions. He al. (2022) propose two-stage method using hierarchical feature modulation and dynamic context feature transformation. ITM-LUT (Guo al., 2023b) combines LUT with AI, achieving an efficient display-end ITM. However, these methods struggle in extremely bright regions due to over-compressed learning targets, as shown in Fig. (c).
Gain Map-based Inverse Tone Mapping. The SDR image is rendered to SDR display for legacy devices, while upgrades to HDR display with the help of GM (Canham et al., 2024). It inspires GM-ITM methods to predict GM for adapting SDR to HDR display. Compared to SI-HDR, which aims to reconstruct scene irradiance, GM-ITM is more akin to EDR-to-HDRTY up-conversion, as they are both ITM tasks. However, GM-ITM shifts the learning target to GM,, which is applied in the display process, thereby achieving a more flexible up-conversion than traditional ITM tasks.
FORMULATION
In this section, we elaborate on the pipeline of restoring linear HDR based on the SDR-GM pair. The detailed process is shown in Fig. 2, which can be divided into the following three steps:
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Linsrrize
Restore
leM
Lu
LADR
Decode
Qmii Queax
Lingurize
8SDR
XMP
ВYDR
L.R
Frenn Date
ISDR
LşDR
Figure 2: The SDR-GM pair to linear HDR pipeline. processes an SDR image ISDR the down- sampled and normalized GM NGM and metadata (shown in the blue frame). to produce linear HDR output for HDR display. The metadata includes the maximum value Qmaz and minimum value Qmin for normalization, as well as the offsets ōsnn and δHDN ensuring non-zero division when computing GM. The path of offsets in the figure is omitted for brevity.
LR
(1) Decode GM. Practically, the GM, denoted as TGM is produced by pixel-wise division HDR and SDR, followed by logarithmic compression, and finally normalization and down-sampling, The maximum and minimum values for normalization, Qmas and Q are stored in the metadata. To decode IGM from the file, we first apply the following equation:
IGM U (Qmaz Qúin + Qmin+
(1)
where NGM denotes the stored normalized and down-sampled GM, and U(.) represents up- sampling function. In practice, the encoding and decoding process usually sets Qmin as zero.
(2) Linearize SDR and GM. To obtain the final linear HDR, we need to linearize both the SDR im- age and GM. To be specific, IsDa linearized to LSDR by electro-optical transfer function (EOTF), while IGM is linearized to LGM by exponential function. The process can be formulated as:
LSDR EO
LGM exp2(lGM),
where E-TF(-) denotes EOTF function, and exp2(-) denotes the exponential function with base 2. (3) Restore HDR. Once the GM is decoded and both the SDR image and the GM are linearized, the linear HDR LHDR can be computed as follows:
LADR = (LSdR + 8SDR) @ LGM 8HDR.
(3)
where denotes element-wise multiplication, δSDR and δHDR are small offsets prevent zero division in the encoding process. In practice, the Eq. 3 can also be simplified to LHDR = LSDR @ LGM shown Fig. (a). More details of the simplification process and GM formation pipeline can be found in Sec. A of the appendix.
4 METHOD
4.1 OVERVIEW
As an intermediary in the ITM process, GM is characterized by high contrast along with fluctuating peak value, which makes direct estimation challenging. To tackle this challenge, we turn to the de- composition GM, i.e., the normalized GM INGM and the corresponding maximum value Qmea- This decomposition is well-suited to the inherent locality and globality characteristics of GM, re- sulting more stable and robust learning process. Building on this, we propose a brancb network named GMNet, which consists of Local Contrast Restoration (LCR) branch estimating INGM from the pixel-level image features, and a Global Luminance Estimation (GLE) branch pre- dicting Qear based the image-level luminance information.
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Input
LCR Branch
Ourput
Local Head
aware Medialation
Mochllttio
Local Tail
Res
Blocks
Res
Blocks
Channel
Res Blncks
ISDR
INGM
GLF Branch
Global Head
Squeeze
Module
Squeeze
Module
Squeeze
Module
Quncx
Weer
Wchn
IGM
Spattal--waare Modulation
Squezze Module (Qmax path) )
Notation
Mu Musipiicaioon
Cony
Rel
Cony
ReLU
Conv
Elemen, Mulippication
Qmax
poto
Chomel-siie Cunvolation
Woer
Wa
Figure 3: Architecture of the GMNet. The proposed network consists of a Local Contrast Restora- tion (LCR) branch, which predicts the normalized GM INGM. and Global Luminance Estima- tion (GLE) branch, which estimates the maximum value Qmaz and provides global guidance to the LCR branch. The details of the spatial-aware modulation and the squeeze module illustrated the bottom. The final IGM is derived from the scalar multiplication of estimated INGM and Qmax- During the training phase, the LCR branch processes the SDR image patch, while inference phase, it handles the full-resolution SDR image. The GLE branch processes the down-sampled full SDR image in both the training and inference phases.
As depicted in Fig. 3, the LCR branch processes the SDR image ISDR, extracts image features, and applies modulation in both spatial and channel dimensions, ending up with the normalized GM TNGM as output. The GLE branch takes the down-sampled SDR image B as input to reduce computational complexity, expands the receptive field, and further squeezes the feature to derive the maximum value Qmaz. In addition to estimating Qmaz. the intermediate features in the GLE branch also serve as the global guidance enhance the restoration in the LCR branch. Specifically, the GLE branch extracts the spatial-aware modulation kernel Wker for spatial feature enhancement, and generates the channel-wis. modulation weights Wehn modulate the regional features along the channel dimension. Finally, IaM is obtained by the multiplication of INGM and Qmaz- then following the pipeline in Sec. to restore the final HDR result.
4.2 LOCAL CONTRAST RESTORATION BRANCH
To promote the restoration of the regional contrast, introduce the LCR branch. not only extracts fine-grained spatial details but also integrates spatial-aware and channel-wise modulation, which incorporate global guidance refine and adjust the local prediction process, ensuring more accurate regional restoration.
illustrated Fig. 3, the input SDR image ISDA initially processed into shallow spatial feature Floc through a local head consisting of three convolutional layers with ReLU activation functions.
The stride the first convolution layer set to 2 for a larger receptive field, facilitating subsequent convolutions to capture more information. The resulting floe is then passed through three cascaded ResBlock groups (He et al., 2016) to extract deeper image features. Spatial-award and channel-ve -wise modulation are applied between the groups effectively incorporate global information. Finally, local tail with a pixel-shuffle layer and three convolutional layers reconstructs the feature into the normalized GM which is denoted JNGM-
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Spatial-aware modulation and channel-wise modulation. During the spatial-aware modulation process, the LCR branch receives a spatial kernel Wker E xx33xx from the GLE branch, which serves global guidance promote contrast restoration. The output feature FY of the first Res- Block group is first passed through a channel-wise convolution layer with spatial kernel Wkers pro- ducing intermediate feature Fmidd This intermediate feature is then processed through secutive 3 x 3 convolutional layers with ReLU activation in the interval. The resulting feature is passed through the Sigmoid function to generate the spatial mask W Wmask. which applied to FT via element- wise multiplication, outputting the modulated spatial features Fsps accordingly.
During the channel-wise modulation process, the LCR branch receives channel weights W Wehn R X from the GLE branch, which are used to modulate the intermediate output F2 of the second ResBlock group through a channel attention mechanism. These proposed modulation mech- anisms enable effective global guidance in the local restoration process, making regional GM esti- mation more precise.
4.3 GLOBAL LUMINANCE ESTIMATION BRANCH
To facilitate the estimation of the fluctuating peak value of GM and provide global modulation guidance to the local contrast restoration process, we introduce the GLE branch to extract global features from the down-sampled SDR image LLR SDD: The proposed GLE branch not only produces an estimation the peak value of the target GM, but also provides the spatial kernel Wker and channel weights W Wehn- Specifically, the input SDR is first convolved to Fglb by the global head, which shares a similar structure with the local head but conducts down-sampling twice. Next, Falb is fed into three parallel squeeze modules to derive the spatial kernel W channel weights Wehn and the estimated peak value Qmaz required for reconstruction.
Squeeze module. To squeeze the global feature of image statisties, the squeeze module first down- samples Fgib through an adaptive pooling layer, which is then expanded to richer feature space by two 1 x convolutions and activation functions to accommodate more complex nonlinear mappings. Finally, a convolutional layer with 1 x filters used to adjust the shape of the final output.
To meet the different needs of guidance and estimation, the squeeze module controls the shape of the output through the pooling size of the first layer and the number of filters in the last layer. Specifically, the 3 3 pooling and C filters in the last layer produce Wker RR333xC while 1 x pooling and C filters are employed to obtain W E IR 1x1xC and Q Qmax E IR is derived from 1 x pooling and convolution with one filter last.
4.4 LOss FUNCTION
Directly supervising INGM and Qmaz can lead imbalanced supervision between tensor and scalar. To address this, we retain direct supervision to INGM and indirectly supervise Qma through IGM. which is the product of INGM and Qmas- The overall loss function in the proposed framework consists of two components. The first is the L1 loss for INGM, defined as follows:
LNGM = |INCN - INGM [NNM-INOM
(4)
where iNGM denotes the estimated normalized GM. The second component is the La loss for TGM. which is defined as follows:
LoM = |CaN - IGM
(5)
where ias denotes the calculated GM from the Eq. 1. To ensure uniform scaling and smooth convergence, IGAs is normalized by the dataset maximum for supervision. summary, the total loss is defined as:
Liotal = an CNGM + agLaM:
(6)
where a and a2 are weights used to balance the different components of the loss function. In our implementation, set on 1.0 and 02 3.0.

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504
SDR
HDBRUNer
KUNNe
EPCE-HDR
IXCDR-UNet
HDCFM
FMNer
ITM-LUT
Qurs
GT
HDRUND
KUNer
EPCE-HDR DCDR-UNet HDCFM
FMNer
ITM-LUT
Ours
5.0
Figure 4: Qualitative comparisons on the synthetic dataset. The left side shows the SDR image and ground truth PQ-encoded HDR image, while the right side displays the output patches and corresponding error maps.
Lincar Domain
PQ Domain
HDR Domain
Category
Method
Params
PSNR1
SSIM
SRSIM
PSNR1
SSIM
SRSIM
AEyri
HDR-VDP3?
HDRUNet
1.57M
41,2579
0,9969
0.9950
40.9315
0.9946
0.9988
3,5799
9,9080
KUNet
1.08M
40.4879
0,9952
0.9943
34,5201
0,9619
0.9986
7.1538
9,7851
SI-HDR
EPCE-HDR
33.79M
41,5919
0.9956
0.9954
31,4700
0.9430
0.9916
8.5830
9.6694
0.9952
0.9962
HDСFM
0.10M
41,8563
0,9967
0.9960
44.9017
0.9992
0.9995
2
9.9115
SDR lo- HDRTV
FMNet
1.24M
40,7699
0,9970
0.9940
44.4798
0.9996
0.9994
2.3032
9.8991
ITM-LUT
0.57M
39.5735
0,9950
0.9912
43.7152
0.9987
0.9987
2.2865
9.7268
GM-ITM
Ours
1.83M
43.5510
0.9977
0.9976
47.6256
0.9998
0.9997
1.6262
9.9477
Table 2: Quantitative comparisons on the synthetic dataset. PSNR, SSIM, and SRSIM are used to evaluate both linear HDR and PQ-encoded HDR. Additionally, ÆETP and HDR-VDP3, metrics specifically designed for HDR evaluation, are employed. Bold text and underline text indicate the best and second-bes performance, respectively.
EXPERIMENTS
5.1 IMPLEMENTATION DETAILS
We use the Adam optimizer (Kingma, 2014) with 81 = 0.9 and 32 0.99 to train our network. The batch size is total of x 10 iterations. The weights of GMNet are randomly initialized. Each ResBlock group contains 5 blocks, and the number of hidden layers is set to 64. The input of the GLE branch TLR SDR is at the reduced 256 x 256 resolution. We use the ReLU activation function in the network. All experiments are conducted on a workstation equipped with RTX 3090 under Ubuntu 20.04 LTS.
32, and the initial learning rate 2 10 halving every x 10a iterations, with
5.2 DATASETS
Synthetic dataset. We collect eleven BT.2020/PQ 4K-UHD HDR videos from the internet, using eight for training and three for testing. To ensure diversity and reduce redundancy, frames are ex- tracted at two-second intervals, resulting in total of 1276 frames. Corresponding SDR images are generated using our degradation model, which incorporates roll-off, gamut mapping, tone mapping, clipping, and quantization (Reinhard et al., 2023; ITU-R, 2017; 2021: 2023). The GM is then cal- culated by Eq. 8, where Qmin, 8mins &max are set to zero. Both the GM and SDR have a resolution of 3840 2160. After filtering, obtain training set of 900 pairs and test set of 100 pairs.
Real-world dataset. To further explore the complexity of the real- world imaging environment, we build real-world dataset to advance research in GM-ITM. We capture images in various natural settings using the Xiaomi 14 Ultra equipped with the LYT-900 sensor, which outputs double-layer by default. The dataset
HDR images in ISO standard (ISO, 2024), and Qminy Smin: &maz
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62X
SDR
HDRIINer
KIINer
EPCE-HIR
IXDDR-LNNet
HDCFM
FMNer
ITM-LUT
Ours
415
GT
HDRUNe:
KUNK
EPCE-HOR
IXCIR-UNer
FMN
ITM-LUT
Ours
Figure 5: Qualitative comparisons on the real-world dataset. The left side shows the SDR image and ground truth PQ-encoded HDR image, while the right side displays the output patches and corresponding error maps.
Linear Domain
PQ Domain
HDR Domain
Category
Method
Params
PSNRT
SSIM
SRSIM7
PSNRT
SSIM
SRSIM
AErre
HDR-VDP37
HDRUNet
0
0.9866
37.3966
0.9969
0.9984
KUNet
108M
33.5256
0.9907
0.9880
33.7895
0.9690
0.9979
9.0578
9.6893
S1-HDR
EPCE-HDR
33.79M
32,7616
0,9914
0.9899
31.1207
0.9466
0.9973
10.4322
9.7218
DNDR-UNet
1.33
33.5421
0,9924
0.9873
38.3252
0.9970
0.9987
4,8645
9,8781
0,9879
0.9882
38.1894
9.8035
SDR-10- HDRTTV
FMNet
1.24M
32,6247
0.9921
0.9828
39.7632
0.9988
0.9986
4,5544
9.8110
ITM-LUT
0.57M
31.6677
0,9842
0.9871
37.9021
0.9958
0.9985
5.4299
9.6831
GM-ITM
Ours
1.83M
33,9490
0.9928
0.9896
40.2088
0.9993
0.9989
4,0260
9.8757
Table Quantitative comparisons on the real-world dataset. PSNR, SSIM, and SRSIM are used to evaluate both linear HDR and PQ-encoded HDR. Additionally, ATTP and LDR-VDPP metrics specifically designed for HDR evaluation, are employed. Bold and underline text indicate the best and second best performance, respectively.
includes diverse scenes, both indoor and outdoor, as well as daytime and nighttime settings, covering wide range brightness levels within some scenes. The SDR image has resolution 4096 x 3072, while the GM is 2048 x 1536, and both horizontal and vertical orientations are included. After filtering out low-quality pairs, we select 900 pairs for training and 100 pairs for testing. More details of the datasets can be found in Sec. B of the appendix.
5.3 COMPARISONS TO OTHER METHODS
Evaluation metrics. To comprehensively validate the performance of HDR-related methods on GM-ITM, we quantitatively compare HDR results across three domains. In the linear domain, where HDR results retain their original linear form with highly varying peak values, directly computing metrics may lead unbalanced comparisons. To address this, we evaluate the HDR results in their normalized form using PSNR, SSIM, and SRSIM (Zhang & Li, 2012) metrics. In the PQ domain, HDR results are encoded with the PQ function, which offers more perceptually reflective measure of visual similarity. Similarly, we normalize the PQ-encoded HDR and employ the same metrics used in the linear domain. Additionally, we use HDR-specific metrics such as AEIT (ITU-R, 2019) and HDR-VDP3 (Mantiuk et al., 2023), which we collectively refer to as the HDR domain.
Compared methods. To the best of our knowledge, there are currently no methods specifically designed for GM-ITM. Therefore, we compare our method with the solutions of closely related tasks, i.c., SI-HDR, and SDR-to-HDRTV up-conversion. For SI-HDR, where the learning target is to restore linear HDR, we use HDRUNet (Chen et al., 2021a), KUNet (Wang et al., 2022), EPCE- HDR (Tang et al., 2023), and DCDR- UNet (Kim et al., 2024) for comparison. For up-conversion. in which the learning target is to restore the PQ-encoded HDR images, compare our method with HDCFM (He et al., 2022), FMNet (Xu et al., 2022), and ITM-LUT (Guo et al., 2023b). All methods are re-trained
proposed datasets to ensure fair comparison.
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GT
Backnone
Direct QM
QM
QM+CW
QM+SA
Full
Figure 6: The qualitative ablation results of the proposed components. maps are shown below the normalized GM estimation results.
The corresponding error
Components
Evaluation Metries
Backhone
QM
CW
SA
PSNR-L1
PSNR-PQ"
PSNR-NGMI
AFSTPl
HDR-VDP31
41.1670
45.2765
29.5436
1.7711
9.9210
V
0
41.3755
44.9717
28.4058
2.1757
9.9128
V
42.3479
46,2067
32.8009
1.8395
9.9431
V
43.0480
47. 47.1238
33.2117
1.6939
9.9454
V
42.9085
47,0011
33.2731
1.7081
9.9442
43.5510
47.6256
33.4729
1.6262
9.9477
Table 4: The quantitative ablation results of the proposed components. The QM, CW, and SA represent Qax prediction, channel-wise modulation, and spatial- modulation. The "-", """"O"" and "V" in the QM column represent not, directly, and indirectly learning Qman-
Quantitative comparison. Tab. 2 lists the quantitative results on the synthetic dataset. Through analysis, we find that the SI-HDR methods achieve better quantitative results in the linear domain, while inferior to the EDR-to-HDRYT different optimization targets lead to bias two domains. An exception is the HDCFM, which efficiently fits the synthetic dataset with lightweight parameters and avoids over-fitting, achieving good results in linear domain. The target of our method neither linear HDR nor PQ-encoded HDR, but the best results achieved in both domains, proving the superiority of targeting GM and the effectiveness of the proposed method.
methods in the PQ domain. It is easy to understand that
The quantitative results the real-world dataset are listed in Tab. 3. Compared to synthetic data, real-world data is derived from a black-box pipeline, features a higher level of noise, and involves more complex imaging environments. Our method still achieves the best results for complex real- world data, proving the generalization and effectiveness of our method. More details of comparison experiments computational efficiency and real-time processing capability be found in Sec. C of the appendix.
Qualitative comparison. As the high dynamic range display is not always available, we follow previous work (Chen et al., 202 Xu et al., 2022), encoding the linear HDR results with PQ-OETF for visualization. Since the SDR display is usually decoded by Gamma-EOTF, the images may appear dark here. In addition, we compute the error maps between the predicted HDR and the GT in the linear domain, which are shown below the patches.
The visualization results of the synthetic dataset and the real dataset are shown in Fig. 4 and Fig. 5, and more visual comparisons can be found Sec. D. Sec. E,, and Sec. F of the appendix. For the SI-HDR methods, the visual results tend towards significant differences over small areas and distortions details. The SDR-to-HDRTV methods are short in the highlight pixels due to the over-compression of the PQ-OETF, and also perform poorly in contrast restoration. Our method can effectively reconstruct the overall luminance and regional contrast details, especially the highlight details, achieving more realistic and compelling visual experience.
5.4 ABLATION STUDIES
Ablation on the proposed components. We conduct an ablation analysis on three main compo- nents of GMNet in the proposed synthetic dataset, and the quantitative results are shown in Tab. 4.

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Hidden Layers
Linear Domain
PQ Domain
HDR Domain
Params
PSNRT
SSIM
SRSIM
PSNRT
SSIM
SRSIMT
AErrel
HDR-VDP31
32
0.46M
42.7029
0,9972
0.9972
46.7620
0.9997
0,9997
1.7093
9.9319
1.03M
43.3299
0.9973
0.9973
47.4524
0.9998
0.9997
1.6990
9.9408
64
1.83M
43.5510
0.9977
0.9976
47.6256
0.9998
0,9997
1.6262
9.9477
Table 5: Ablation study on the model size of GMNet. The original implementation of our network is 64 hidden layers.
Down Scale
Lineur Domain
Domain
HDR Domain
Model
PSNR
SSIM
SRSIMT
PSNR
SSIMT
SRSIMT
AErre
HDR- VDP31
HDRUNet
41.2579
0.9969
0,9950
40,9315
0.9946
0.9988
3.5799
9,9080
x1
HDCFM
41.8563
0,9967
0,9960
44,9017
0.9992
0,9995
2.4773
9.9115
Ours
43.5510
0.9977
0.9976
47.6256
0.9998
0.9997
1.6262
9,9477
eDRUNet
39.3861
0,9969
0,9952
40.6861
0.9969
0.9993
2.7233
9.9038
x2
HDCFM
39.6308
0.9964
0,9953
44,4200
0.9993
0.9995
2.6179
9.9178
Ours 41,2254
0.9974
0.9972
46.5649
0.9998
0.9997
1.7124
9.9461
HDRUNet
38.9650
0.9970
0.9951
39.9763
0.9962
0,9992
2.7883
9.9046
x4
HDCFM
39,2906
0.9966
0.9961
44.3981
0.9993
0.9995
2.4795
9.9178
Ours
40.5103
0.9972
0.9966
46.4938
0.9998
0,9996
1.7672
9.9413
Table 6: Ablation study on the resolution of GM. We compare the results of HDRUNet (Chen al., 202 la), HDCFM (He et al., 2022), and our method in three different down- samplsan scales.
Compared to not learning Qmaa- learning Qmax directly gets better linear performance, but the im- balanced supervision between tensor and scalar causes a decline in perception metrics, and all the results have significant decay compared to indirectly learning Qmaz. Respectively introducing the spatial-awan modulation or channel-wise modulation improves the network performance, and applying both makes the full net obtaín the highest quantitative results.
We also visualize the predicted normalized GM for the ablation study, and the results are shown in Fig. 6. The experiment without Qmaz path obtains poor estimation both locally and globally, while achieving significant improvement by attaching it. The sole application of spatial-aware modulation or channel-wise modulation makes a modest increment, and the application of both achieves further visual improvement. The results of both quantitative and qualitative ablation experiments validate the effectiveness of the proposed components.
Ablation on the model size of GMNet. Keeping the network structure unchanged, we adjust the number of hidden layers control the model size. The results of the ablation experiments on the synthetic dataset are shown in Tab. 5. The experimental results show that reducing model param- eters does not lead to significant performance degradation, especially in SSIM-L,, SSIM-PQ and HDR-VDP3 that measure perception quality, verifying that our method can maintain accuracy with reduced resources.
Ablation on the resolution of G.. As mentioned in Sec. 3, GM is usually down-sampled to reduce file size. Therefore, we conduct ablation experiments on three different down-scales of GM on the synthetic dataset, analyzing the effect of resolution on up-conversion quality as shown in Tab. 6. For different down-sampling factors, our method is kept in a leading position. Though the increment slightly decays with the larger multiples, our method requires less computation while the other methods remain the same, proving the effectiveness of our method.
6 CONCLUSION
Motivated by a novel double-layer HDR image format, we introduce a Gain Map-based Inverse Tone Mapping (GM-ITM) task, for which specially design network named GMNet. Based on the analysis of GM characteristics, we introduce a Local Contrast Restoration (LCR) branch and Global Luminance Estimation (GLE) branch for GMNet, which capture pixel-wise and image- wise information for GM estimation. Moreover, we first present synthetic and real-world datasets consisting of SDR-GM pairs to promote the research of GM-ITM. Experimental results demonstrate the superiority of our method in qualitative and quantitative evaluations.
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ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation of China under Grants 62131003 and 62021001.
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A GM FORMATION PIPELINE
Corresponding to the SDR-GM pair to HDR pipeline introduced in Sec. 3, we illustrate how to calculate GM from the SDR-HDR pair in this section. First, the GM is computed by the following equation:
LADR ÖHDR
IaM logz
LSDR 6şDn
where a common empirical value for ÓSDR and SHDR 1/64. The Eq. can also be conducted as follows:
loga
LHDR
LSDR + 0,
TaM
LSDR
LSDR = 0,
which is free of offsets ösnn and öHDN, allowing Eq. 3 to be simplified to:
LUDR LSDR @ Lam-
To reduce file size, the calculated IaM is then normalized using the following equation:
INGM = IcM - Qmin
(10)
Qmax Qmin
LLR Finally, the TNGM will be down-sampled to NGM for further compression, and LLR may be gamma-encoded in some cases.
In practice, many implements (Apple, 2021; Google, 2024) recommend using the linear luminance YSdr and YEdR substitute LSDR and LøDR in Eq. 7 or Eq. 8, thus making IaM gray- scale GM. To our knowledge, the existing devices can only produce gray-scale GM. Therefore, all experiments in this paper performed on the single-channel GM, and the three-channel GM is beyond our scope.
B DETAILS OF DATASETS
We investigate the datasets employed in SI-HDR and SDR-to-HDRTV up-conversion tasks, and compare them with our proposed synthetic and real-world dataset in Tab. 8.
Besides, demonstrate the diversity of the synthetic dataset and real-world dataset in Fig. 7 and Fig. 8, which includes outdoor, indoor, daytime, and nighttime scenes, providing a wide range of content with different brightness levels and texture types.
C REAL-TIME PERFORMANCE EVALUATION
To evaluate the computational efficiency and real-time processing capability of our method, we conduct comparison experiments in Tab. 7.
Method
Params,
Runtime
MACs|
PSNR-L1
PSNR-PQ1
ÆETTP
HDRUNet 1.57M
613.89ms
4161.75G
33.1686
37.3966
5.7358
KUNet
1.08M
621.70ms
7534.50G
33.3256
33.7895
9.0578
33.79M
23992.84ms
146412.14G
32.7616
31.1207
10.4322
DCDR-UNet
1.83M
1085.91ms
4502.77G
33.5421
38.3252
4.8645
HDCFM
0.10M
497.05ms
127.08G
32.7963
38. 1894
6.2957
FMNet
1.24M
304.13ms
4147.13G
32.6247
39.7632
4.5544
ITM-LUT
0.57M
19.81
59.16G
31.6677
37.9021
5.4299
Ours
1.83M
75.26ms
1112.18G
33,9490
40.2088
4.0260
Table 7: Efficiency comparisons on the real-world dataset. The runtime is evaluated in NVIDIA A100 the average of 100 trials in the resolution of 4096
3072.
As can be seen, benefiting from the simple form of the target GM. our method achieves the second fastest runtime. While the look-up-table- base solution ITM-LLU is faster, its performance on image quality metrics is much lower.
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Input
Ourput
Dataset
Volume
Resolution
Type
Depth
Chunnel
Type
Depth
Channel
Cordts al. (2016)
2975 images
2048 1024
LDR
RGB
HDR
16bit
RGB
SI-HDR
Pérez-Pellitero al. (2021)
1494 images
x900x 1060
LDR
8hit
RGB
HDR
16bit
RGB
Kim al. (2019)
39840 patches
160x 160
SDR
YUV
HDR
16bit
SDR-to- HDRTV
Zeng et al. (2020)
23229 patches
1080x 1080
SDR
Sbit
YUV
HDR
10bit
YUV
al. (2021b)
1235 images
3840x2160
SDR
RGB
16bit
RGB
Guo et (2023a)
3878 images
3860x
SDR
8bit
RGB
HDR
16bit
RGB
synthetic dataset
900 images
3840x2160
SDR
Shit
RGB
GM
8bit
GRAY
GM-ITM
real-world dataset
900 images
4096x3072
SDR
RGB
8bit
GRAY
Table 8: Details on the training of different HDR datasets. The image resolution of some datasets varies, thus we list the highest resolution of each dataset. Our datasets stand out for the first and second highest resolution and the unique GM output.
SDR
GM
IDR
Outdoor
Indoor
Daytime
Nighttime
Figure 7: The diverse scenes, including outdoor, indoor, daytime, and nighttime settings, in the proposed synthetic dataset. The depicted HDR images are tone-mapped by the method proposed by Liang et al. (2018).
SDR
GM
IDR
Outdoor
Indoor
Daytime
Nighttime
Figure 8: The diverse scenes, including outdoor, indoor, daytime, and nighttime settings, in the proposed real-world dataset. The depicted HDR images are tone-mapped by the method proposed by Liang et al. (2018).
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D ADDITIONAL VISUAL COMPARISONS ON DIVERSE SCENES
In this section, we provide more qualitative results for visual comparisons between our method and existing methods. For the results the synthetic dataset shown Fig. 9, our method reconstructs better edges than other methods, achieving the best visual quality in the highlights. We also visualize estimation results on a real-world nighttime scene in Fig. 10. Our method stands out for best perceptual quality and minimum errors, proving the effectiveness of our method in night scenes.
R02
SDR
HDRUNe
KUNN
E.CF-HDR
DCDR-UNNe
HDCFM
FMNer
ITA-EUT
Oas
HDRUNet
KUNet
EXCE-IDR DCDR-UNet HDCFM
TMNet
ITM-LUT
Ours
Figure 9: Qualitative comparisons on the synthetic dataset. The left side shows the SDR image and ground truth PQ-encoded HDR image, while the right side displays the output patches and corresponding error maps.
235
SDR
HDRUNet
KUNet
EPCE-HDR
DCDR-UNGt
HDCFM
FMNNe
ITM-LUT
Ours
100
30
GT
HDRRNet
KUNet
EXCE-HDR DCDR-UNet
HDCFM
FMNet
IMM-LUT
Ours
Figure 10: Qualitative comparisons on the real-world dataset. The left side shows the SDR image and ground truth PQ-encoded HDR image, while the right side displays the output patches and corresponding error maps.
E ADDITIONAL VISUAL COMPARISONS ON CHALLENGING CONDITIONS
To enable more in-depth analysis in challenging conditions, perform qualitative experiments in Fig. 1l and Fig. 12.
As shown in Fig. 11, our method achieves superior performance in the region extreme bright- ness. The HDCFM also achieves low errors, but with grid effect due to operator properties. Fig. 12 shows the estimation results the leaves with complex textures and caves with sharp edges, and our method demonstrate superior performance on complex textures in the real-world scenario, validating the generalization and the robustness of the proposed method.
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0.04
0.03
SDR
HDRUNet
KUNet
EPCE-HDR
DCDR-UNee
0.02
HDCEM
FMNet
ITM-LUT
Ours
0.00
Figure 11: Qualitative comparisons on the synthetic dataset. The left side shows the SDR image and ground truth PQ-encoded HDR image, while the right side displays the output patches and corresponding error maps.
0.40
SDR
HDRUNet
KUNet
EPCE-HDR
DCDR-UNet
0.20
0.15
HDCFM
FMNet
ITM-LUT
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Figure 12: Qualitative comparisons on the real-worke dataset. The left side shows the SDR image and ground truth PQ-encoded HDR image, while the right side displays the output patches and corresponding error maps.
F ADDITIONAL VISUAL COMPARISONS ON MORE DATASETS
The qualitative comparisons of diverse images from HDR-SYNTH (Liu et al., 2020). HDR- REAL (Liu et al., 2020), HDREye (Nemoto et al., 2015), and VDS (Lee et al., 2018b) datasets meaningful and help increase the credibility of experiments. The results of the qualitative
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Published as a conference paper at ICLR 2025
experiments are shown in Fig. 13. Experimental results show that method recovers local and global contrast well and achieves smooth and realistic visual results, verifying its generalizability over wider range of data.
LDIt
KUNei
FMNel
KUNet
HD SYR-SYSTT
HDDR-EALL
HDR REAL
HDREye
HDRBSye
VDS
VDS
Figure 13: Qualitative comparisons the HDR-SYNTH (Liu al., 2020), HDR-REAL (Liu al.,
2020), HDREye (Nemoto al., 2015), and VDS (Lee et al., 2018b) datasets.
Figure 14: The thumbnails of the proposed synthetic dataset. It provides a wide range of content with different brightness levels and texture types.
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Figure 15: The thumbnails of the proposed real-world dataset. It provides wide range of content with different brightness levels and texture types.
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Reviews:
[{"review_id": "zmBcCqJa8E", "reviewer": "Reviewer_yo1C", "summary": "This paper introduces Gain Map-based Inverse Tone Mapping (GM-ITM), focusing on estimating the Gain Map (GM) for SDR images rather than directly converting to HDR. The proposed dual-branch network, GMNet, effectively combines local and global information for accurate GM prediction. Extensive experiments on both synthetic and real-world datasets demonstrate GMNet’s advantages over existing HDR methods, showing improved performance in both quantitative and qualitative metrics. The paper also contributes new datasets for GM-ITM research, supporting future advancements in HDR image processing.", "questions": "1. **Insufficient Dataset Generation Information**: The process and diversity of the synthetic and real-world datasets are not fully detailed.\n2.  **Lack of Real-Time Performance Evaluation**: The paper does not address GMNet’s computational efficiency or real-time processing capabilities, which are critical for practical HDR applications, especially on mobile devices.\n3. **Scalability of the Model**: It’s unclear if GMNet can scale effectively with higher-resolution images, which is essential as HDR media often demands large image resolutions for detail preservation.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Innovative Task Definition**: The paper introduces a novel task, Gain Map-based Inverse Tone Mapping (GM-ITM), which focuses on GM estimation rather than direct HDR prediction, leveraging a unique double-layer HDR format for enhanced up-conversion.\n\n2. **Effective Network Design**: The proposed GMNet utilizes a dual-branch structure with Local Contrast Restoration (LCR) and Global Luminance Estimation (GLE) branches, effectively capturing both pixel-level and image-level information for accurate GM prediction.\n\n3. **Comprehensive Dataset Contribution**: The authors provide both synthetic and real-world SDR-GM datasets, which are diverse and well-suited for evaluating GM-ITM, fostering further research in this field.\n\n4. **Strong Experimental Validation**: Extensive quantitative and qualitative experiments demonstrate GMNet’s superiority over existing HDR-related methods, showcasing its potential in real-world applications.", "weaknesses": "1. **Limited Error Analysis**: The paper provides little insight into errors GMNet might produce in challenging conditions (e.g., extreme lighting, high contrast), which is crucial for understanding its limitations.\n\n2. **Model Size Trade-Offs**: An ablation study on model size versus performance could reveal if a smaller GMNet version maintains accuracy with reduced resources, benefiting applications needing speed-accuracy balance.", "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": "nhz32pf7vb", "reviewer": "Reviewer_cLvc", "summary": "The paper introduces a new task, Gain Map-based Inverse Tone Mapping (GM-ITM), to address the problem of converting standard dynamic range (SDR) images into high dynamic range (HDR) images using a Gain Map (GM). This work aims to improve HDR up-conversion by focusing on GM estimation instead of directly predicting HDR, leveraging a dual-branch network (GMNet) that includes Local Contrast Restoration (LCR) and Global Luminance Estimation (GLE) branches. To support the research, the authors also propose synthetic and real-world datasets to evaluate the method. The experiments demonstrate GMNet’s quantitative and qualitative superiority over existing methods for HDR-related tasks.", "questions": "A more detailed discussion about the computation complexity would help understand the applicability of the proposed method.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Novelty: The paper addresses an emerging area in HDR up-conversion by proposing the GM-ITM task, which is relatively unexplored and offers an innovative approach to inverse tone mapping. \n\nMethod: The proposed dual-branch GMNet architecture is well-designed, with LCR and GLE branches targeting local and global image features respectively, which allows for improved GM estimation. \n\nEvaluation: The authors perform extensive quantitative and qualitative evaluations on synthetic and real-world datasets, benchmarking GMNet against well-established HDR methods and showing clear improvements. \n\nDataset: The creation of both synthetic and real-world datasets for GM-ITM facilitates further research and addresses a gap in data availability, providing a valuable resource for the field. \n\nAblation Studies: The paper includes thorough ablation studies on key components, such as spatial-aware modulation and GM resolution, to substantiate the effectiveness of GMNet’s design choices.", "weaknesses": "Limited Comparison Scope: The paper primarily compares GMNet with existing HDR and SDR-to-HDRTV up-conversion methods, but a comparison with more diverse or advanced inverse tone mapping techniques could strengthen the results. \n\nComplexity and Computation: The dual-branch network adds computational overhead, especially when processing high-resolution images. Discussion on efficiency or real-time applicability is limited, which could impact practical usage. \n\nDependency on New Data Format: The proposed method relies heavily on the novel GM format, which is not yet widely adopted. This dependency could limit the method's applicability outside specialized devices or formats. \n\nVisual Comparisons in Real-World Scenarios: Although qualitative results demonstrate GMNet’s advantages, additional challenging real-world scenarios could better highlight its robustness. For instance, extreme lighting conditions or complex textures may expose limitations.", "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": "e2fMUOylJJ", "reviewer": "Reviewer_nA6K", "summary": "With recent advancements in display technology and the widespread adoption of HDR displays, a new dual-layer image format has emerged that is compatible with existing SDR displays. In response to this, the authors propose a new type of inverse tone mapping algorithm that infers a gain map applicable to adaptive HDR displays. Additionally, the authors construct an additional dataset for training and evaluating this type of network. Through experiments, the authors demonstrate that their method restores HDR images more effectively and reliably compared to other approaches.", "questions": "* Could the authors explain why the GM can be transmitted at a reduced resolution compared to the original SDR?\n* Could you share experimental results for directly learning Q_max adjusted for the number of pixels?\n* Would it be possible to release the code and data to ensure reproducibility?\n\n* If I have misunderstood any part or if my concerns are adequately addressed during the review process, I would be more than willing to increase my score.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "* The authors' proposed inverse tone mapping algorithm, which takes real display environments into account, is both highly practical and innovative.\n* The authors have effectively organized the existing methods in this field through Table 1. I believe this will be very helpful for future researchers studying this area.\n* They have provided a formulation for the proposed method, which makes it easier to understand the approach proposed by the authors.", "weaknesses": "* The authors need to conduct additional survey on SI-HDR. According to [A], SI-ITM is broadly divided into two branches of research: 1) methods that directly reconstruct HDR images and 2) methods that reconstruct bracketed exposures. In my opinion, there is a need to provide a detailed description of the differences between Learning from LDR stacks methods and generating a gain map.\n\n* Furthermore, the authors lack comparisons not only in terms of performance for learning from LDR stacks but also in terms of time efficiency with existing methods.\n  * Like [B], it is possible to create images with relative EV +1 / -1 and expand the dynamic range. A discussion should be added on whether GM-ITM is more efficient than this case or not.\n\n* As the authors propose a new type of network, they need to provide a detailed structure of this network. \n\n* There is a lack of validation on datasets commonly used in inverse tone mapping.\n   - HDR-SYNTH + HDR-REAL: Yu-Lun Liu et al. Single-image hdr reconstruction by learning to reverse the camera pipeline. CVPR, 2020\n   - HDREye: Hiromi Nemoto et al. Visual attention in ldr and hdr images. 9th International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), 2015.\n  - VDS: Siyeong Lee et al. Deep chain HDRI: Reconstructing a high dynamic range image from a single low dynamic range image. IEEE Access, 2018\n\n[A] Lin Wang and Kuk-Jin Yoon, Deep Learning for HDR Imaging: State-of-the-Art and Future Trends, TPAMI, https://arxiv.org/abs/2110.10394\n\n[B] Ning Zhang et al. Revisiting the Stack-Based Inverse Tone Mapping, CVPR, 2023", "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": "YzUBVTmjFO", "reviewer": "Reviewer_6tzz", "summary": "The paper introduces a dual-branch network GMNet to estimate the Gain Map for Inverse Tone-mapping. The paper also builts a synthetic and a real-world dataset from the existing HDR resources and mobile devices, respectively.", "questions": "Although the idea is interesting, it is not new and quite similar to estimating the transfer function which is well-known in image restoration domain. That said the contribution of the paper is limited. \n\nThe literature reviews and compared methods are outdated which makes the paper less convincing. The latest method that was compared is from 2022 (KUNet) while the most recent one is DCDR-UNet: Deformable Convolution Based Detail Restoration via U-shape Network for Single Image HDR Reconstruction (based on Google Scholar).\n\nIn term of  PSNR in HDR domain, there is another metric which is more common than PNSR-NGM named mu-PSNR. The paper should consider running the evaluation on this metric too.\n\nIt is not clear about Q_max. Q_max value should not be in the range of [0,1], is that correct? If so, I_GM is not in the range [0,1] either. And can not normalize it as well, the reviewer wonder how the loss can be calculated and how the model was converged? \n\nFinally, as there is a upsampling function when estimating I_GM, the reviewer think that that I_GM might not represent the real I_GM precisely due to the interpolation function when applying upsampling (which can be either nearest neighbors or bidirectional).", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The idea of estimating the Gain Map instead of the HDR value is interesting, which can become a baseline for any future research.", "weaknesses": "The methods that were used for comparison in the paper are outdated.\nThe method is not really novel.\nLack of a common metric in HDR domain.\nThere are also some technical issues about loss function, data and models.", "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)