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REVIEW 4 major objections 6 minor 1 cited by

Deep Lidar-guided Image Deblurring

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Depth maps from a smartphone Lidar, fused through lightweight adapters, measurably improve neural image deblurring, with Depth-Restormer gaining 2.10 dB PSNR on ARKitScenes.

desk verdict First real-Lidar deblurring study with a sensible adapter and informative ablations, but the causal claim outruns the synthetic-blur evidence and the missing uninformative-depth control. read the letter →

arxiv 2412.07262 v1 pith:QZMW4TCT submitted 2024-12-10 cs.CV eess.IV

Ziyao Yi , Diego Valsesia , Tiziano Bianchi , Enrico Magli This is my paper · ORCID
classification cs.CVeess.IV
keywords imagedeblurringLidardepthdepth-guidedcontinuallearningmultimodalfusionsuper-resolutionadaptersARKitScenes
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 asks whether the depth maps produced by the small Lidar sensors now found in smartphones can improve the quality of neural image deblurring. The authors argue that they can, because Lidar measures scene geometry actively and is therefore immune to the motion blur that corrupts the RGB exposure. They build a generic recipe: take any state-of-the-art encoder-decoder deblurring model, freeze its encoder, attach lightweight depth adapters in the decoder that modulate image features with depth features, and train the adapters plus decoder on paired RGB-depth data. On ARKitScenes frames blurred with standard benchmark kernels, the depth-enhanced models consistently beat their RGB-only baselines, with Depth-Restormer gaining 2.10 dB PSNR. The claim matters because it identifies a practical, already-deployed sensor as a way to regularize an ill-posed inverse problem without retraining large models from scratch.

What carries the argument

The machinery has three pieces. The depth super-resolution block is a small network that upsamples the mobile Lidar map (e.g., $256\times192$) to full image resolution while preserving sharp, piecewise-constant depth edges; it is pretrained on ARKitScenes pairs of iPad Lidar and Faro Focus S70 depth, and its final projection layer is removed so it outputs deep depth features rather than a pixel map. The depth adapter sits at each decoder scale of the pretrained model: it processes depth features with a convolutional attention that mimics the second-order statistics of the guided filter, passes them through a sigmoid, multiplies them onto the image features, and concatenates the conditioned features back through a lightweight transformer block. The continual learning protocol freezes the encoder of the deblurring model, keeps the decoder trainable, and trains the adapters on paired multimodal data, requiring only a small update to $\theta_g$ and learning the new $\theta_h$. Together these components implement the factorized estimator $f_\theta(y,d)=g_{\theta_g}(y)\circ h_{\theta_h}(d)$, making the depth-guidance mechanism portable to any encoder-decoder restoration network.

What would settle it

Capture real low-light handheld-blurred photos with a phone that also records Lidar depth (using a tripod sharp reference or a burst-aligned reference), run Depth-Restormer against Restormer on those frames, and check whether the reported 2.10 dB PSNR advantage survives; if it collapses, the synthetic-blur premise is the breaking point.

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Extended reading notes

Core claim

The central claim is that an independent, blur-free depth measurement from a mobile Lidar—even at the low $256\times192$ resolution of the iPad Pro sensor—carries genuine regularizing information for image deblurring, and that this information can be injected into any state-of-the-art neural deblurring model through a universal adapter. The paper demonstrates this by factorizing the joint estimator as $f_\theta(y,d)=g_{\theta_g}(y) \circ h_{\theta_h}(d)$, where the unimodal deblurrer $g_{\theta_g}(y)$ is kept largely pretrained and the depth processor $h_{\theta_h}(d)$ is learned. The depth branch super-resolves the Lidar map with a network trained to preserve piecewise-constant edges, then uses convolutional attention adapters that generalize the classical guided filter to modulate decoder features at multiple scales. On ARKitScenes with simulated blur, depth-enhanced models outperform their baselines across four architectures, and $8\times$ super-resolved mobile depth matches the guidance quality of a high-end Faro Focus S70 Lidar. An ablation shows real Lidar depth (+2.10 dB) beats depth estimated from the blurry image itself (+1.27 dB), supporting the argument that true independent side information, not just extra image features, drives the gain.

Load-bearing premise

All measured gains come from synthetic blur applied to sharp ARKitScenes frames, so the paper assumes real handheld blur behaves like those benchmark kernels and that the iPad Lidar depth is accurately registered and filled without bias.

Editorial extensions

If this is right

  • Any encoder-decoder deblurring model, including Restormer, Stripformer, NAFNet, and DeblurDiNAT, can be converted into a depth-guided version with a few extra parameters and a small training budget, without training from scratch.
  • Mobile Lidar depth, after learned $8\times$ super-resolution, provides guidance equivalent to a high-end Faro Focus S70 Lidar for deblurring, at 36.62 versus 36.59 dB PSNR.
  • Depth estimated from the blurry image itself helps less than true Lidar depth (+1.27 versus +2.10 dB), suggesting that independent geometric side information, not just extra image features, is the source of the improvement.
  • Freezing the encoder while training the decoder and adapters outperforms full fine-tuning by 0.45 dB, so the continual-learning recipe is the effective way to add the modality.
  • The gain is not uniform: it is large for Restormer, modest for DeblurDiNAT, and nearly null for NAFNet, so depth guidance is most valuable when the base model is not already near its performance ceiling.

Reading between the lines

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

  • Because all quantitative results use synthetic blur applied to sharp ARKitScenes frames, the 2.10 dB advantage has not yet been demonstrated on real handheld motion blur; a natural next step is a real low-light capture experiment with paired depth.
  • The architecture-agnostic design suggests the same adapter recipe could extend Lidar guidance to other restoration tasks such as denoising, super-resolution, or video deblurring, where an independent depth stream is available.
  • The paper's own limitation note predicts that gains should shrink on flat, textureless surfaces and on distant scenes; this could be tested by stratifying deblurring results by scene depth range and edge density.
  • The reported sensitivity to depth-RGB misalignment is not quantified; a practical robustness test would jitter the depth map by a few pixels and measure how quickly the PSNR advantage degrades.
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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. The paper proposes a method to integrate low-resolution mobile Lidar depth maps into existing neural image-deblurring models. The core ingredients are a lightweight depth super-resolution network that maps iPad-Lidar depth to the image resolution while preserving edges, a set of neural adapters that modulate decoder features with depth features in a way inspired by the guided filter, and a continual-learning strategy that freezes the encoder of a pretrained deblurring network and trains the decoder plus adapters. Experiments are run on a subset of ARKitScenes with real iPad Lidar depth and blur simulated by convolution with Levin et al. kernels. The method is applied to Restormer, Stripformer, DeblurDiNATL, and NAFNet, with reported PSNR gains of +2.10, +1.17, +0.19, and +0.04 dB, respectively. Ablations address the value of neural super-resolution over bicubic interpolation, the comparison between Lidar depth and depth estimated from the blurred image, the adapter versus input concatenation, and the choice of freezing the encoder.

Significance. If the central claim holds, the paper would provide useful evidence that an independent, blur-free depth measurement from a smartphone-class Lidar can regularize neural deblurring, which is a novel and practically relevant direction. The approach is architecture-agnostic and is demonstrated on four recent deblurring models, which is a genuine strength. The use of real consumer-Lidar depth data from ARKitScenes, rather than synthetic depth, is also commendable, as is the inclusion of ablations that isolate the super-resolution, fusion, and training-strategy choices. However, the evidence base is narrower than the abstract suggests: the blur is synthetic and spatially invariant, the causal role of depth content versus added network capacity is not established, there are no error bars or statistical tests, and there is no real-blur validation. These gaps prevent the paper from fully supporting its stated conclusions, but they are addressable with additional experiments, so the contribution is potentially sound rather than fundamentally flawed.

major comments (4)
  1. [§IV-A, Table I] The comparison that supports the central claim ("None" vs. "Depth-*") changes both the input modality and the network architecture at the same time. For Depth-Restormer, the parameter count rises from 26.1M to 30.0M, and for Depth-NAFNet from 17.1M to 23.7M, while the training protocol also changes through the addition of adapter modules and a depth-super-resolution network. The reported gain of +2.10 dB for Depth-Restormer therefore conflates the effect of depth content with the effect of added capacity and of a learned feature-refinement module that might improve performance even with an uninformative depth input. A control experiment that keeps the adapter and training protocol identical but supplies an uninformative depth signal (e.g., a constant map, Gaussian noise, or scene-shuffled depth) is needed to attribute the gain to depth content. The NAFNet result (+0.04 dB with +6.6M parameters) makes a pure-capacity explanation less likely, but it does not substitute for the missing control.
  2. [§IV-A] All quantitative evidence is obtained on synthetically blurred images: sharp ARKitScenes frames are convolved with a set of standard benchmark kernels following Levin et al. This is a spatially invariant, linear degradation, whereas the motivating application is handheld motion blur in low light, which is typically spatially varying and accompanied by sensor noise and rolling-shutter artifacts. The abstract claims validation on "a dataset with real-world depth data," but the depth is real while the blur is not. To support the practical claim, the authors should add an evaluation on real blurred images with associated Lidar depth, or at least on a realistic blur benchmark (e.g., GoPro-style frame averaging or a recent blur-synthesis method), and report whether the depth-guided gains persist. Section V mentions limitations of the Lidar sensor but does not test them experimentally.
  3. [§IV-C, Tables I–IV] All reported PSNR/SSIM/LPIPS values come from a single run on a validation set of 500 randomly sampled pairs, with no error bars, no multiple seeds, and no statistical significance tests. Differences such as +0.04 dB (Depth-NAFNet) and +0.19 dB (Depth-DeblurDiNATL) may be within run-to-run variation, and the "+0.0012" delta for DeblurDiNATL in Table I is actually an LPIPS increase (worse). The paper should report mean and standard deviation over at least three independent training runs, or per-image confidence intervals, so that the smaller gains can be assessed. This is particularly important because the paper's main claim is a causal one about the utility of depth, not merely a qualitative architectural comparison.
  4. [§III-B2, §IV-A] The proposed adapter mechanism relies on the assumption that the depth map and the RGB image are accurately registered and that invalid depth pixels are filled without introducing bias. The paper states that invalid pixels are discarded during depth-super-resolution pretraining but does not describe the alignment preprocessing between the iPad Lidar depth and the RGB camera, nor does it report any analysis of depth-RGB misalignment or its effect on deblurring. Since the adapters are designed to align depth edges with image edges, this is a load-bearing assumption. The authors should add a sensitivity experiment with, for example, small translations or rotations of the depth map relative to the RGB image, to quantify the robustness of the method to registration errors.
minor comments (6)
  1. [§II-A, §IV-A] There is a citation inconsistency: Restormer is cited as [26] in Section II-A but as [37] in Section IV-A and Table I. Reference [37] in the bibliography is a different paper (Zhong et al., video deblurring).
  2. [Table I] The LPIPS deltas for DeblurDiNATL and NAFNet are reported as positive numbers although the depth-enhanced models have higher (worse) LPIPS values. The sign should be negative, or the table should indicate that lower is better and deltas reflect an increase.
  3. [Throughout] Several typos should be corrected: "belurring" (Section II-A), "inpaiting" (Section III-B1), "demostrate" (Section IV-B), and "finetuning" (used inconsistently as "fine-tuning").
  4. [§III-B1] The description of the depth super-resolution pretraining omits important details: the input and output resolutions are not explicitly stated, and it is unclear at which feature resolution the last projection layer is removed. Please specify the architecture dimensions and the resolution at which deep features are extracted.
  5. [§II-B] The term "continual learning" is used loosely; the proposed method freezes the encoder and trains the decoder and adapters, which is closer to standard fine-tuning or parameter-efficient adaptation. The authors should clarify the relationship to continual learning and, if they intend it as continual learning, describe how catastrophic forgetting is avoided beyond the partial freezing.
  6. [Fig. 2] The architecture diagram is dense and the text labels are small; consider enlarging the figure or splitting it into panels for the depth block and the adapter so that the flow of depth features and image features is clearer.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical comparison against external baselines on a public dataset, and no derivation step reduces to its own inputs.

full rationale

The paper's central claim—that mobile Lidar depth maps can improve deblurring—is supported by supervised training and evaluation on the public ARKitScenes dataset with external baselines (Restormer, NAFNet, Stripformer, DeblurDiNATL). The depth super-resolution network is pretrained on paired low-resolution iPad Lidar and high-resolution Faro Lidar depth maps using an L1 loss, which is independent of the deblurring PSNR metric; the deblurring models are then trained and evaluated on synthetic blur with and without depth conditioning. There is no equation-to-equation reduction in which the predicted quantity is defined by or fitted from the target metric. The comparison against estimated depth (Depth Anything) further attempts to isolate the contribution of true depth. The only substantive concern is an internal-validity gap—the depth-guided models also add parameters, and no uninformative-depth control is included—but that is a confound or experimental-design limitation, not circularity. No load-bearing self-citations or imported uniqueness theorems are present. The paper is self-contained as an empirical study.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

All free parameters are architectural choices; no physical constants or new physical entities are introduced. The central claim rests on dataset and blur-simulation assumptions rather than on a mathematical derivation, so the circularity burden is low.

free parameters (3)
  • Depth super-resolution scale = 8x
    Chosen to match iPad Lidar 256x192 depth to RGB resolution; Table III shows 4x and 8x were tested, so the scale is selected by the authors.
  • Number of adapter insertions = 3
    Three depth blocks and adapters are placed in the decoder, one per resolution level as shown in Figure 2; this is an architectural choice, not derived from data.
  • Adapter transformer attention heads = 1
    The lightweight transformer in the adapter uses a single attention head to constrain complexity, as stated in Section III-B2.
assumptions (4)
  • domain assumption Convolving sharp images with Levin benchmark kernels produces realistic motion blur
    Introduced in Section IV-A; the paper never validates this simulation against real blurred captures with Lidar.
  • domain assumption ARKitScenes iPad Lidar depth maps are true, blur-free, and registered with the RGB images
    Relied on throughout Sections III and IV; no alignment error analysis is provided.
  • domain assumption Depth features extracted from a super-resolution network trained on ARKitScenes transfer to the deblurring training distribution
    The depth SR network is pretrained on ARKitScenes and then finetuned jointly, as described in Section III-B3; cross-dataset transfer is not tested.
  • domain assumption Freezing the encoder preserves useful image features while the decoder absorbs depth conditioning
    Chosen in Section III-B3 and validated only by the ablation in Table IV, not by a theoretical argument.

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Cite this review

Pith. "Pith review of Deep Lidar-guided Image Deblurring." pith.science (2026). https://pith.science/paper/QZMW4TCT

@misc{pith2026241207262,
  author       = {Pith},
  title        = {Pith review of: Deep Lidar-guided Image Deblurring},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QZMW4TCT}},
  note         = {Machine review of arXiv:2412.07262}
}
read the original abstract

The rise of portable Lidar instruments, including their adoption in smartphones, opens the door to novel computational imaging techniques. Being an active sensing instrument, Lidar can provide complementary data to passive optical sensors, particularly in situations like low-light imaging where motion blur can affect photos. In this paper, we study if the depth information provided by mobile Lidar sensors is useful for the task of image deblurring and how to integrate it with a general approach that transforms any state-of-the-art neural deblurring model into a depth-aware one. To achieve this, we developed a universal adapter structure that efficiently preprocesses the depth information to modulate image features with depth features. Additionally, we applied a continual learning strategy to pretrained encoder-decoder models, enabling them to incorporate depth information as an additional input with minimal extra data requirements. We demonstrate that utilizing true depth information can significantly boost the effectiveness of deblurring algorithms, as validated on a dataset with real-world depth data captured by a smartphone Lidar.

Figures

Figures reproduced from arXiv: 2412.07262 by the authors.

Figure 1
Figure 1. Pipeline of a generic depth-guided image deblurring model. The encoder part of the deblurring pretrained model is frozen, while the decoder part is [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The depth-restormer architecture example. The adapter is added on each level of the decoder part, which is in front of the transformer block. There [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Depth super-resolution architecture. models. A high-level overview of them is presented in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Left to right: Mobile Lidar depth map, Restormer deblurred image, Depth-Restormer, ground truth. As shown in the detail, Depth-Restormer has [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Visual comparison of deblurring results for the considered state-of-the-art models. For each scene, the top row presents results of the original model, [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Comparison of depth maps. From left to right: ground truth image, blurred image, high-resolution depth map from Faro Focus S70 Lidar, super-resolved [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: The neural-network-super-resolved iPad depth map better preserves sharp edges after the upscaling operation, leading to improved deblurring [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A novel method and dataset for depth-guided image deblurring from smartphone Lidar

    eess.IV 2025-09 conditional novelty 6.0 of 10

    Lidar depth-guided deblurring via a zero-shot diffusion method, evaluated on a new 45-scene dataset, achieves the best perceptual quality (LPIPS).

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.