Title: UltraLED: Learning to See Everything in Ultra-High Dynamic Range Scenes
Venue: NeurIPS 2025
Accepted: yes
Keywords: High dynamic range

Low-light image enhancement

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


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for achieving ultra-high dynamic range (UHDR) imaging from a single short-exposure RAW image. The core insight lies in the observation that RAW images possess higher bit depth and more predictable noise characteristics compared to processed images. As a result, even short-exposure RAW captures retain comprehensive scene information, offering substantial potential for low-light image enhancement. The proposed pipeline consists of three main stages: (1) a U-Net architecture is used to predict a ratio map ; (2) this ratio map  guides the generation of a well-exposed image; and (3) a lightweight U-Net is subsequently trained to denoise the generated image.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\nThe author summarizes the problems faced by traditional RGB-based HDR imaging methods and proposes a solution based on RAW image format, providing a new solution to related problems.\n\nWeaknesses：\n1.The novelty of this paper is limited. The core components of the proposed method are directly derived from prior work. For example, in Section 3.2, the key component of the method—the ratio map —is adopted from [9] (line 172). In Section 3.1, the strategy for synthesizing artificial overexposure is taken from [38]; the HDR fusion technique is based on [3]; and the noise modeling approach relies on [2].\n2.The references, baseline methods, and even the methodological choices are outdated. The authors are encouraged to incorporate more recent studies from the past few years to ensure the timeliness and relevance of their research.\n3.The simulation of artificial overexposure in Section 3.1 involves several handcrafted operations, such as adding random highlight patches, using bilateral filtering, and applying Gaussian blur to mimic light diffusion. However, the authors do not provide sufficient details regarding the parameter settings, the underlying rationale for these operations, or any analysis of their impact on the final imaging results.\n\nQUESTIONS:\n1.Please refer to weaknesses.\n2.In the comparative analysis, the authors highlight the challenges faced by single-image HDR methods that rely on RGB inputs, implicitly emphasizing the advantages of their single-frame RAW-based approach. However, this comparison may not be entirely fair. RAW images inherently contain significantly more information than RGB images, which are typically post-processed and compressed. As such, it is questionable whether comparing a single RAW image to a single RGB image provides a balanced evaluation. In fact, a single RAW image often has a larger file size and richer content than several RGB frames combined. This raises the question of whether a more appropriate baseline would involve multiple RGB images rather than just one.\n\nLIMITATIONS:\nRefer to weaknesses and questions, please add relevant explanations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper presents a method for reconstructing ultra-high dynamic range (UHDR) images from a single short-exposure RAW image. The proposed two-stage framework performs exposure correction and brightness-aware denoising to recover details in dark regions. A custom dataset with 9-stop bracketing is used for training, and experiments show significant improvements over existing single-frame methods.

STRENGTHS AND WEAKNESSES:
The proposed method achieves excellent performance, as shown in Tables 1, 2, and 3, outperforming previous methods such as HDRTVNet, RetinexMamba, and others.

In addition, the authors also build a corresponding dataset for benchmarking UHDR reconstruction performance. However, the synthesized UHDR images lack thorough validation, making it uncertain whether they can accurately evaluate the model's performance.

Moreover, the writing quality of the paper is quite poor, and it's difficult to clearly understand the contributions. Is the paper merely proposing a data synthesis pipeline? The network architecture seems to be entirely based on previous methods.

As mentioned in Section 4.5, the strong dependence of the noise model on camera-specific parameters makes it difficult to apply the method in real-world scenarios.

QUESTIONS:
Is this the first paper to address HDR reconstruction from a single short-exposure RAW image?

In Section 4.5, the method shows good performance only on specific camera types, but fails to generalize to others, which is a significant limitation. Could the authors provide some experimental results to demonstrate how poorly the model performs on different types of cameras without retraining?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 1
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

comments: SUMMARY:
This papers presents a new single-shot method for capturing UHDR content. The method relies on heavily underexposed RAW images, which are then adaptively exposed based on new learned ratio maps, and brightness-aware denoising. The results are of very high quality, translating naturally to video as well. The networks used are simple and efficient, making the method highly practical. The quantitative and qualitative comparisons clearly show the superiority of the method, and the ablations demonstrate the effectiveness of the whole approach.

STRENGTHS AND WEAKNESSES:
- Strengths:
- A very practical method that checks all the boxes compared with previous works.
- Simple and effective 2-step approach that makes the problem very tractable.
- Very high quality results, way ahead of previous works.
- Clear presentation of problem, context, method and evaluations.
- A carefully created UHDR dataset that may help future research in this space.

- Weaknesses:
- As the authors acknowledge, different cameras may have different noise profiles, which would require retraining the noise models. However, it is would be very feasible for different vendors to train those modules themselves.

QUESTIONS:
Figure 3 in the paper shows more colorful results than the ground truth, which could be in principle more desirable, but at the same time it can introduce some inconsistencies. For example, in the last row, the ground truth shows an evenly desaturated green mouth region, while the produced result has a more uneven distribution. Is this coming from the use of standard isp and exposure fusion for the tone mapping? Could there be some extra steps to mitigate it?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies reconstruction of ultra-high dynamic range scenes from a single short-exposure RAW image. It proposes a two-stage pipeline—first predicting a pixel-wise ratio map for adaptive exposure correction, then applying a brightness-aware noise model to guide denoising—fully leveraging RAW’s high bit depth. Contributions include (1) decoupling exposure and noise restoration, (2) a novel data synthesis workflow combining simulated over-exposure, HDR fusion, and realistic noise modeling, and (3) a purpose-built UHDR dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The paper is technically sound, with a clear problem formulation and well-justified two-stage design that decouples exposure correction from denoising.\n\n- The motivation for single-frame RAW-domain UHDR reconstruction is crisply presented: avoiding misalignment and ghosting in dynamic scenes.\n\n- Demonstrates that a single short-exposure RAW frame suffices to recover both highlights and deep shadows, a capability critical for real-world night and mixed-lighting scenarios.\n\nWeaknesses\n\n- The core concern is that the paper’s main claim—single-frame RAW reconstruction avoids the ghosting artifacts of multi-frame exposure fusion in dynamic scenes—can only be validated by directly comparing against strong multi-frame fusion baselines (e.g., UltraFusion). Without such head-to-head experiments, the asserted advantage of using a single frame remains unproven and the contribution loses its significance.\n\n- The use of two UNets and multi-step data synthesis may incur higher runtime or memory requirements than existing single-stage RGB-domain approaches; a detailed analysis of inference speed and model footprint is missing.\n\n[1] UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion\n\nQUESTIONS:\nCould you include experiments on scenes with motion (e.g., moving objects or camera motion) and compare against a state-of-the-art multi-frame fusion method such as UltraFusion?\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}