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REVIEW 4 major objections 6 minor 58 references

RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A single state-space model unifies sRGB-to-RAW reconstruction for photos and video.

desk verdict A genuinely unified Mamba-based sRGB-to-RAW de-rendering architecture with large reported PSNR gains, but the unqualified state-of-the-art claim is undercut by an unexplained video SSIM regression and third-party baselines. read the letter →

arxiv 2411.11717 v1 pith:2AH3VK74 submitted 2024-11-18 cs.CV

Hongjun Chen , Wencheng Han , Huan Zheng , Jianbing Shen This is my paper · ORCID
classification cs.CV
keywords sRGB-to-RAWde-renderingRAWreconstructionmetadata-guidedstatespacemodelMambaunifiedimage-videoISPinversionlocaltonemapping
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that sRGB-to-RAW de-rendering—recovering the camera's original linear sensor readings from a processed sRGB image—can be accomplished with one network for both still photos and video. Previous methods relied on task-specific architectures: image de-rendering uses a few sampled RAW pixels as guidance, while video de-rendering uses the first frame of an sRGB-RAW pair as guidance. RAWMamba unifies these by treating every form of guidance as an (sRGB, RAW) pair and learning a mapping from the sRGB side that is then applied to the RAW side. The paper reports that this single framework improves PSNR by 3.37 dB on the CAM image benchmark and by 2.26 dB on the RVD-Part2 video benchmark over the previous best task-specific models, without any online fine-tuning. The authors interpret this as evidence that a metadata-agnostic, state-space-model-based pipeline can replace incompatible image-specific and video-specific de-rendering systems.

What carries the argument

The central object is the Unified Metadata Embedding (UME) module, which converts any metadata type—sampled RAW pixels for images or a first-frame sRGB-RAW pair for video—into a common (sRGB, RAW) pair. UME encodes the target sRGB, the metadata sRGB, and the metadata RAW into a shared feature space, forms a soft affinity matrix $A_1 = \mathrm{Softmax}(F_{\mathrm{srgb}}^T F'_{\mathrm{srgb}})$, and multiplies it by the RAW metadata features to produce a global embedding. A second branch adds position encodings (fixed for images, optical-flow-warped for video) and a deformable transformer offset to produce a local embedding; the two are summed into the final metadata embedding. The other component is LTA-Mamba, a bidirectional state space model with two scanning orders—3D Hilbert for local tone-mapping context and raster line-by-line for global consistency—combined with channel attention to reduce feature redundancy. Together these components turn sparse metadata into dense, globally consistent reconstruction cues that drive the final RAW reconstruction.

What would settle it

Train RAWMamba on the three CAM cameras and then evaluate it on a fourth camera of the same dataset (or on a camera with a materially different ISP) without fine-tuning, comparing its PSNR against a per-camera fine-tuned variant; if the cross-modal transfer claim is correct, the single offline model should stay within a small margin, while a large gap would contradict it. A more direct test is to apply the sRGB-derived affinity matrix alone to warp the RAW metadata toward the target RAW and check whether the warped RAW approximates the ground truth before any learned refinement, which would isolate whether the transfer is doing the work.

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Extended reading notes

Core claim

The central claim is that the alignment mapping between the de-rendering sRGB image and the sRGB part of any metadata (sampled pixels or a first frame) transfers to the RAW domain, because sRGB and RAW are two modalities of the same underlying scene information. Using this mapping, the Unified Metadata Embedding (UME) module computes a global affinity matrix for ISP-wide operations like white balance and gamma correction, plus a locally corrected attention map with optical-flow-adjusted position encodings for local tone mapping, then uses both to pull aligned reference information out of the RAW part of the metadata. The reconstruction side adds a Local Tone-Aware Mamba (LTA-Mamba) module that scans features along a 3D Hilbert curve to capture local spatio-temporal context, then scans line-by-line for global consistency, propagating the sparse metadata cues over the entire image or video. The paper reports state-of-the-art results: 53.13 dB PSNR on the CAM image benchmark and 51.97 dB on the RVD-Part2 video benchmark, outperforming task-specific models without any online learning steps.

Load-bearing premise

The load-bearing premise is that the alignment mapping learned between the sRGB image and the sRGB part of the metadata also holds for the RAW part of the metadata, because sRGB and RAW are treated as two views of the same scene information; if that transfer fails for cameras or ISP pipelines unlike those in the training set, the metadata embedding would be misaligned and the reported gains would not generalize.

Editorial extensions

If this is right

  • One trained model can serve both still and video capture on the same camera, eliminating the need for separate image and video de-rendering deployments.
  • The 2.26 dB video gain over the previous chained model suggests that first-frame metadata can be propagated through long video sequences more effectively with linear-complexity Mamba scanning than with sequential CNN architectures.
  • If the cross-modal UME transfer holds, different metadata formats (sampling rate, first-frame, or other partial RAW cues) can be handled without redesigning the network, simplifying camera-side metadata selection.
  • The local-then-global scan design indicates that explicitly modeling local tone mapping helps invert the ISP even when the available RAW information is extremely sparse, which is directly relevant to low-bandwidth capture scenarios.
  • Because the image benchmark gain of 3.37 dB is obtained by the same unified model that also handles video, the paper's results imply that joint training across the two modalities does not degrade image-side reconstruction quality.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The transfer premise yields a testable prediction: UME should work for metadata types the paper did not try, such as a low-resolution RAW thumbnail or an ISP parameter vector, as long as an (sRGB, RAW) pair can be assembled; treating any partial information as such a pair would be a natural extension.
  • The use of a 3D Hilbert curve for local scanning suggests a spatial-locality prior, so ablating by substituting other space-filling curves (such as serpentine or Morton order) would reveal whether topological locality alone drives the gain or whether the specific curve matters.
  • If the sRGB-to-RAW mapping is truly modality-independent, the same affinity-based alignment could be exported to other paired modalities (e.g., depth-plus-RGB or multispectral pairs), turning UME into a general cross-modal alignment module rather than a de-rendering-specific one.
  • The reported numbers cover three cameras and one video subset, so a broader stress test across many camera ISPs would clarify whether the 'inherently applicable' claim generalizes beyond the training distribution.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. This paper introduces RAWMamba, a unified architecture for sRGB-to-RAW de-rendering that handles both images and videos within a single network design. The method comprises two modules: the Unified Metadata Embedding (UME) module, which aligns sampled pixel metadata (images) or first-frame metadata (videos) with the sRGB input through a global affinity matrix and a local deformable attention mechanism, and the Local Tone-Aware Mamba (LTA-Mamba) module, which uses local (3D Hilbert) and global (raster) scans to propagate the sparse metadata across the whole frame or sequence. The authors report state-of-the-art results on the CAM image benchmark (53.13 dB PSNR vs. 49.76 dB for CAM) and on the RVD-Part2 video benchmark (51.97 dB PSNR vs. 49.71 dB for RVD), along with an ablation of the proposed modules.

Significance. The motivation for a single architecture covering image and video de-rendering is practical and the proposed design is technically sound. The use of a state-space model for long-range metadata propagation is a reasonable extension of recent Mamba-based vision models, and the reported ablations show clear contributions from each module. If the empirical results withstand a careful re-evaluation under a strictly fair protocol, the gains over task-specific baselines are substantial and would make this a strong candidate for publication. However, the video SSIM regression and the reliance on externally reported baseline numbers are concerns that must be addressed before the claims can be fully trusted.

major comments (4)
  1. [Section 4.3, Table 2] RAWMamba reports a PSNR of 51.97 dB on RVD-Part2, exceeding RVD by 2.26 dB, but its SSIM (0.9965) is lower than RVD's (0.9983). Since the training loss in Eq. (18) includes an SSIM term with weight 0.5, this regression is surprising and is not discussed in the paper. Please provide an analysis of this trade-off, verify that the SSIM computation is identical to that used for the RVD baseline (e.g., same handling of 4-channel RAW, same borders), or report additional perceptual metrics that support the claim of higher reconstruction quality.
  2. [Sections 4.2 and 4.3] The comparisons against RIR, SAM, INF, and RVD rely on numbers reproduced or reported by other papers, not on re-runs with the authors' own code. Given the observed SSIM discrepancy, the evaluation protocol could differ between methods. Please either re-run these baselines under the exact same evaluation settings, or provide a detailed description of the protocol (SSIM computation, data pre-processing, border handling, etc.) and ensure it is consistent with the baseline papers.
  3. [Sections 1 and 4.1] The paper claims a 'unified' model for image and video, but the experiments train image and video models separately with different hyperparameters (batch size, epochs, input size). It is not demonstrated that a single set of weights can handle both tasks. Please clarify whether the architecture is unified but the models are task-specific, or provide a joint-training experiment and report the resulting performance.
  4. [Section 3.3] The UME module relies on the assumption that a mapping learned between sRGB data and the sRGB component of metadata is 'inherently applicable to the RAW domain.' This is a strong claim that is not empirically supported. Please provide evidence (e.g., an ablation that uses mismatched metadata, a visualization of the affinity matrix, or a discussion of why this transfer is justified) or temper the statement to reflect that the mapping is learned jointly in an end-to-end framework.
minor comments (6)
  1. [Title] The title contains an unintended space: 'RA WMamba' should be 'RAWMamba'.
  2. [Section 4.1] The phrase 'We preserves the original splits' should be 'We preserve the original splits'.
  3. [Section 3.3] The sentence 'to encode the raw and sRGB images into a shared latent feature space' is ambiguous because both x_srgb and x'_srgb are sRGB for the input and metadata; consider revising to 'to encode the inputs and metadata into a shared latent feature space'.
  4. [Section 3.4.1] The notation for the depth-wise convolution is inconsistent: Eq. (15) uses 'DWC' while Figure 3 describes 'DWConv'.
  5. [References] References [1] and [2] are identical (Brooks et al.) and should be merged or renumbered.
  6. [Section 4.1] The paper does not report model size, FLOPs, or runtime, despite the efficiency motivation for Mamba; please add these numbers to support the efficiency claims.

Circularity Check

0 steps flagged · score 0.0 of 10

Not circular: end-to-end supervised training with held-out splits; the UME transfer assumption is a design assumption, not a definition, and the self-cited RVD baseline is an external comparison point rather than a load-bearing premise.

full rationale

This is a supervised metadata-conditioned reconstruction paper, not a derivation. The network f is trained end-to-end with L = L_mse + 0.5 L_ssim (Eq. 18) against ground-truth RAW y, and tested on held-out splits: CAM uses the same train/validation/test splits as CAM [33], and RVD-Part2 'preserves the original splits of training and test sets.' The metadata (first frame or sampled pixels) is a partial ground-truth input, not a fitted constant; predicting full RAW from partial RAW is interpolation, not a prediction that equals its input by construction. The UME module's statement that 'since sRGB and RAW data represent two different modalities of the same information, this mapping is inherently applicable to the RAW domain as well' is an architectural assumption, not a circular reduction: the mapping is learned, and its validity is judged by held-out PSNR/SSIM. The only self-citation of note is the RVD video benchmark/baseline [54], whose authors overlap with this paper (Wencheng Han, Jianbing Shen). However, the paper uses RVD's reported numbers as an empirical comparison point ('we directly compare our results with those reported in the paper'), not as a premise that defines the method or forces the outcome; the comparison is externally falsifiable and does not reduce the reported improvement to the input. The unaddressed SSIM regression on RVD-Part2 (0.9965 vs RVD's 0.9983 in Table 2) and the 'state-of-the-art' phrasing are empirical/correctness concerns, not circularity. No step in the paper equates an output to an input by definition, fits a parameter and then predicts the fitted quantity, or imports a uniqueness theorem from overlapping-authors work.

Assumptions & free parameters 3 free parameters · 4 assumptions · 2 invented entities

The paper's central claim rests on a learned mapping between sRGB and metadata, an assumed transfer of that mapping to RAW, and the efficacy of Mamba scans for propagating sparse reference information. No part of the approach is derived from first principles or verified with machine-checked proofs; the numerical results are the only support.

free parameters (3)
  • loss balance weight lambda = 0.5
    Hand-set in Eq. 18 to combine MSE and SSIM losses; no sensitivity analysis is provided.
  • metadata sampling rate = 1.5%
    Inherited from CAM [33] for image experiments; no ablation over this rate is shown.
  • number of LTA-Mamba modules = 3
    Set by the authors; no ablation over this count is shown.
assumptions (4)
  • ad hoc to paper sRGB and RAW are two modalities of the same information, so a mapping learned between sRGB and metadata sRGB is applicable to RAW.
    Invoked in Section 3.3 to justify UME; not proven or empirically verified.
  • domain assumption A state space model (Mamba) can propagate sparse metadata information globally across pixel sequences.
    Assumed in Section 3.4; based on prior Mamba literature, not demonstrated here independently.
  • domain assumption The 3D Hilbert curve preserves local spatiotemporal structure sufficient for modeling local tone mapping.
    Assumed in Section 3.4.1; no analysis of what structure is preserved or lost.
  • ad hoc to paper Comparisons against RIR, SAM, INF, and RVD are fair when using numbers reported in other papers.
    Sections 4.2 and 4.3 rely on third-party reproduced numbers without re-running under identical conditions.
invented entities (2)
  • Unified Metadata Embedding (UME) module
    purpose: Aligns arbitrary metadata types (sampled pixels or first-frame RAW) with the input sRGB to produce a unified metadata embedding.
    Architectural component introduced in this paper; no falsifiable prediction outside its reported performance metrics.
  • Local Tone-Aware Mamba (LTA-Mamba) module
    purpose: Uses local and global scan strategies to propagate sparse metadata information across the image or video.
    Architectural component introduced in this paper; its design is tied to the local tone mapping assumption, but it makes no independently testable prediction.

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Cite this review

Pith. "Pith review of RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model." pith.science (2026). https://pith.science/paper/2AH3VK74

@misc{pith2026241111717,
  author       = {Pith},
  title        = {Pith review of: RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2AH3VK74}},
  note         = {Machine review of arXiv:2411.11717}
}
read the original abstract

Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW information. In image-based de-rendering, metadata is commonly obtained through sampling, whereas in video tasks, it is typically derived from the initial frame. The distinct metadata requirements necessitate specialized network architectures, leading to architectural incompatibilities that increase deployment complexity. In this paper, we propose RAWMamba, a Mamba-based unified framework developed for sRGB-to-RAW de-rendering across both image and video domains. The core of RAWMamba is the Unified Metadata Embedding (UME) module, which harmonizes diverse metadata types into a unified representation. In detail, a multi-perspective affinity modeling method is proposed to promote the extraction of reference information. In addition, we introduce the Local Tone-Aware Mamba (LTA-Mamba) module, which captures long-range dependencies to enable effective global propagation of metadata. Experimental results demonstrate that the proposed RAWMamba achieves state-of-the-art performance, yielding high-quality RAW data reconstruction.

Figures

Figures reproduced from arXiv: 2411.11717 by the authors.

Figure 1
Figure 1. Comparison with Previous sRGB-to-RAW De￾rendering Approaches. (a) In image de-rendering methods like [25, 33, 36], sampled RAW data is utilized as metadata, fol￾lowed by spatial recovery operations to extract information. (b) For video de-rendering methods [54], the first frame serves as metadata, with a sequential model applied for information extrac￾tion. (c) Our RAWMamba method presents a unified framework capabl… view at source ↗
Figure 2
Figure 2. Overview of the Proposed RAWMamba. In RAWMamba, inputs are processed through the UME module and the main network. The UME module encodes images and metadata into feature spaces, generating global and local metadata embeddings via affinity-based blocks (GEB and LEB). The main network query the metadata embeddings to refine features, which are aggregated input into the LTA￾Mamba module. LTA-Mamba employs local and glo… view at source ↗
Figure 3
Figure 3. The details of the LTA-Mamba module. The LTA￾Mamba module relies on two consecutive Mamba blocks. In the figure, the DWConv block represents a depth-wise convolution layer, while CA denotes a channel attention block. (a) The imple￾mentation of bi-direction mamba. (b) An illustration of the global scan strategy and the local scan strategy. A2 = Softmax (Fsrgb + PE)(F ′⊤ srgb + PE¯ ) √ d + B ! , (11) where d is the di… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Visual comparisons on image dataset CAM and video dataset RVD-part2. As shown in this figure, our unified model achieves the highest accuracy and lowest error compared to other methods, across both image and video datasets. 4. Experiments 4.1. Experiments setup Dataset…

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.