Title: Learning Differential Pyramid Representation for Tone Mapping
Venue: NeurIPS 2025
Accepted: yes
Keywords: Computational Photography

Tone Mapping
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/).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nThe authors should thoroughly address all issues raised above concerning the clarity of the paper.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}