LAN: Learning to Adapt Noise for Image Denoising

Changjin Kim; Sungyong Baik; Tae Hyun Kim

REVIEW 3 major objections 5 minor 56 references

LAN: Learning to Adapt Noise for Image Denoising

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Adding a learnable pixel-wise offset to a noisy input, while keeping the pretrained denoiser frozen, improves denoising on unseen noise.

desk verdict A simple, genuinely new test-time adaptation trick—freeze the denoiser and learn a per-pixel input offset—with consistent but modest gains, and a mechanism story that is plausible but not yet proven. read the letter →

arxiv 2412.10651 v1 pith:FEMTVZKW submitted 2024-12-14 cs.CV cs.AI

Changjin Kim , Tae Hyun Kim , Sungyong Baik This is my paper · ORCID

classification cs.CVcs.AI

keywords imagedenoisingtest-timeadaptationself-supervisedlearninginputnoiseunseenreal-worldlearnableoffsetblind

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 claims that when a pretrained denoiser meets an image with unseen noise, the better move is to adapt the input rather than the network: add a learnable pixel-wise offset to the noisy image so its noise moves closer to the distribution the denoiser was trained on. The authors keep the denoiser frozen and optimize only the offset with a self-supervised loss, and report consistent PSNR and SSIM gains over full-network, first-layer, last-layer, and meta-learning adaptation across DnCNN, Restormer, and Uformer on real-world noise datasets. If true, this reframes test-time adaptation as input-side correction, which is cheaper and avoids the overfitting that degrades fine-tuned networks, and it opens a research direction orthogonal to self-supervised denoising.

What carries the argument

The central object is the pixel-wise noise offset $\phi$ added directly to the noisy input. It is trained by minimizing a self-supervised denoising loss, specifically the Zero-Shot Noise2Noise and Neighbor2Neighbor losses, with the pretrained denoiser held frozen. The offset is meant to approximate $-\epsilon_{s\to u}$, the deviation that separates the unseen noise from the noise the network was trained on, thereby translating the input into $y_{u\to s} = y_u + \phi \approx x_u + e_s$. This input-side translation carries the whole argument: all adaptation happens in the image, not in the network weights.

What would settle it

Compute a statistical distance such as KL divergence or maximum mean discrepancy between the residual noise of the LAN-adapted image and the noise distribution the denoiser was trained on, across the PolyU and Nam test sets; if PSNR improves while this distance does not shrink, the mechanism is unsupported. A second check is to run LAN on images whose noise already matches the training distribution; if it still gives the same gain, the improvement is not explained by noise-distribution matching.

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Extended reading notes

Core claim

The central claim is that unseen noise $e_u$ can be written as seen noise $e_s$ plus a deviation $\epsilon_{s\to u}$, and that subtracting a learned approximation of that deviation from the input image, rather than changing the pretrained denoiser, lets the frozen network denoise as if the noise were familiar. Formally, the method optimizes $\phi = \arg\min_\phi \| f_{\theta^*}(D_1(y_u+\phi)) - D_2(y_u+\phi) \|_2^2$ with $f_{\theta^*}$ frozen, then estimates the clean image as $f_{\theta^*}(y_u+\phi^*)$. The paper reports that this outperforms adapting all or part of the network, with the largest gain on Restormer going from 38.03 dB to 38.86 dB on SIDD-to-Nam with the ZS-N2N loss, and shows qualitatively that the adapted image's noise histogram shifts toward the training noise distribution.

Load-bearing premise

The load-bearing premise is that minimizing the self-supervised loss on the adapted input actually pulls the noise distribution toward the training distribution; the paper asserts this in Section 3.2 and supports it only with one qualitative histogram, without a proof or quantitative measure.

Editorial extensions

If this is right

A pretrained denoiser can be adapted to a new noise type without any weight updates, removing the risk of overwriting or overfitting the network during test-time adaptation.
The input-side offset produces gains within 5 to 20 adaptation iterations across all tested backbones and both self-supervised losses, with the largest reported gain on Restormer from 38.03 dB to 38.86 dB on SIDD-to-Nam with ZS-N2N.
LAN is computationally cheaper than full-network adaptation for 256x256 images, using roughly 74 to 93 percent of the runtime and 74 to 93 percent of the memory of full-trainable adaptation in the reported settings.
Because the offset is optimized per image with a frozen network, the method is orthogonal to the choice of self-supervised loss and could be stacked on top of future self-supervised objectives.

Reading between the lines

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

A natural extension is to apply input-side adaptation to other restoration tasks such as super-resolution, deblurring, or low-light enhancement, where a pretrained model faces a distribution shift in the degradation.
The per-pixel offset could be constrained to a low-rank or smooth parametric form to remove the image-size-dependent memory cost the authors list as a limitation.
Combining LAN with a small amount of model adaptation may yield gains larger than either alone, since the offset fixes input statistics while fine-tuning could correct remaining network-side bias.
One could test whether the learned offset behaves like an anti-adversarial perturbation by measuring its norm and spatial structure and comparing it to adversarial noise of similar magnitude.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this paper does something new. Instead of fine-tuning the denoiser to an unseen noise distribution, it freezes the network and optimizes a per-pixel additive offset on the noisy input, using the same self-supervised loss (ZS-N2N or Neighbor2Neighbor) that others use for test-time adaptation. The empirical claim holds up across three backbones (DnCNN, Restormer, Uformer), two self-supervised losses, and two unseen-noise datasets, with consistent gains of roughly 0.2–0.8 dB PSNR over the pretrained baseline. For a subfield that mostly tweaks architectures or training losses, this is a distinct and useful direction.

What the paper does well: the idea is clearly motivated and explained; the evaluation is reasonably thorough, including comparisons to full-network, first-layer, last-layer, and meta-learning adaptation; and the authors include a small sanity check (Section 4.5, Figure 6) showing that adapted noise looks closer to the training noise distribution. The code is public. The paper is also honest about its main limitation: the number of trainable parameters scales with input image size.

Where the soft spots are: the load-bearing assumption in Section 3.2—that minimizing the self-supervised loss pulls the input noise toward the pretraining distribution D_s—is not actually established. The evidence is one qualitative histogram on synthetic Gaussian/Gamma noise and visual inspection. The loss is a consistency between downsampled views; its minimizer does not obviously correspond to removing the deviation e_s→u. It could be smoothing or other content-dependent manipulation. This does not kill the empirical result, but it means the mechanism story is speculative. The comparison also lacks error bars or variance estimates, and the per-method learning rates are hand-tuned, which matters when gains are under 1 dB. The meta-learning baseline is described in less detail than the others, so the comparison may not be perfectly fair.

I want to push back on one worry in the stress-test note: the circularity concern is actually mild. Performance is measured on clean PSNR/SSIM, not on the self-supervised objective, so the evaluation is not directly fitting the metric.

Bottom line: this is a solid, well-scoped contribution for researchers working on test-time adaptation in image restoration. It deserves a serious referee. The authors should be asked to either demonstrate the distribution-matching mechanism with a quantitative distance measure on real noise, or soften the claim and present LAN purely as a practical TTA method. Either way, it deserves review time.

Referee Report

3 major / 5 minor

Summary. The paper proposes LAN, a test-time adaptation method for image denoising that keeps a pretrained denoiser frozen and instead optimizes a pixel-wise additive offset on the given noisy image using a self-supervised loss such as ZS-N2N or Neighbor2Neighbor. The adapted image is then passed through the frozen denoiser. Experiments compare LAN with full-network, first-layer, last-layer, and meta-learning adaptation on SIDD-pretrained DnCNN, Restormer, and Uformer evaluated on PolyU and Nam, reporting consistent PSNR/SSIM gains and reduced runtime/memory relative to full fine-tuning. A qualitative histogram experiment on synthetic Gaussian and Gamma noise is offered as evidence that the offset moves the input noise toward the training noise distribution.

Significance. If the empirical gains are robust, LAN is an interesting and orthogonal contribution: it avoids modifying network weights, is memory-efficient for large backbones, and opens an input-side view of test-time adaptation. The paper ships code and evaluates three backbones, two self-supervised losses, and two target real-world datasets, which is a strength. The evaluation is not circular, since the offset is optimized with a self-supervised loss while performance is measured on clean PSNR/SSIM. However, the significance is limited by the unverified mechanism and the lack of statistical controls; without these, the contribution is an empirical technique whose working principle remains unsupported.

major comments (3)

[3.2, Eq. (12)] The central claim that minimizing the self-supervised loss with respect to the offset pulls the input noise toward the training distribution Ds is asserted but not demonstrated. Equation (12) is a consistency loss between two downsampled views after the frozen denoiser, not a distributional divergence between noise residuals. An unconstrained pixel-wise offset can lower this loss by smoothing the image or by exploiting the downsampling operators D1/D2, without making the residual statistically similar to the training noise. The only supporting evidence is Figure 6, which shows marginal histograms for one synthetic setup, and Figure 4, which is qualitative. Matching marginal histograms does not establish spatial noise statistics. Please provide a quantitative distributional measure (e.g., MMD or KL divergence on estimated residuals) before and after adaptation, and preferably an ablation where the offset is constrained or where a different loss is optimized, to show that the mechanism is noise-distribution matching rather than generic input alteration.
[Table 1] The central empirical claim of consistent improvement lacks statistical support. No error bars, standard deviations, or significance tests are reported, and the comparisons use per-method learning rates chosen by the authors (Section 4.1), so the relative gains could depend on hyperparameter tuning. Please report variance across images or runs, a sensitivity analysis over learning rates and iteration counts, and ideally paired significance tests. Without these, differences of 0.1-0.3 dB may not be distinguishable from run-to-run variability, and the claim that LAN 'consistently outperforms' full-trainable adaptation is not fully supported.
[Section 4.2, Figure 5] The argument that full-trainable adaptation is fundamentally weaker than LAN is based on a single longer-iteration curve (Uformer with ZS-N2N on Nam). Since the comparison is sensitive to optimization schedules, this one instance is not enough to establish that full-trainable adaptation cannot reach comparable performance with more iterations or better tuning. Please show similar curves for the other backbone/loss/target combinations or explain why this instance is representative. Otherwise the conclusion remains limited to the tuned 20-iteration protocol.

minor comments (5)

[Section 4.1] The initialization of the offset phi is not specified; if it is initialized to zero, state so explicitly, since the optimization is otherwise not fully specified and the semantics of the learned offset depend on the starting point.
[Figure 3 caption] The caption states that 'Full-trainable and LAN (Ours) finetuned the pretrained network via ZS-N2N', which is inaccurate for LAN, since LAN does not modify the network parameters; please rephrase to clarify what is adapted in each method.
[References] References [17] and [18] are the same Neighbor2Neighbor paper with the same authors and venue; please merge them and renumber the citations accordingly.
[Table 2] The header 'Time Memory' is ambiguous; please label the columns as runtime ratio and memory ratio with explicit units or a note that they are percentages relative to the full-trainable baseline.
[Section 4.3, Table 3] The zero-shot comparison is not controlled for training budget or initialization: a randomly initialized DnCNN is trained for more than 1K iterations per image, whereas the other methods use a SIDD-pretrained network adapted for at most 20 iterations. This limits the strength of the conclusion that the large training set is the decisive factor; please add a matched-budget comparison or soften the claim.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation: LAN's offset is optimized per test image against a self-supervised loss and evaluated on clean PSNR/SSIM, so the reported gains are not fitted to the target metric; the unverified distribution-matching assumption in Sec. 3.2 is a mechanism gap, not circularity.

full rationale

Walking the derivation chain: the paper's decomposition eu = es + eps_s->u (Eq. 5) is a tautology, but the offset phi is not derived from that decomposition by construction; it is optimized per test image by minimizing the self-supervised loss in Eq. 12, using only the noisy input and a frozen pretrained network, and it is evaluated by PSNR/SSIM against clean images. The clean image is never used during adaptation, so the reported improvements are not fitted to the evaluation metric. No fitted parameter is renamed as a prediction, and no result is equivalent to its input by definition. The main weakness is that Sec. 3.2 asserts, rather than proves, that minimizing Eq. 12 brings the input noise closer to the seen distribution Ds; the only evidence is the qualitative histogram in Figure 6. That is an unverified assumption about the mechanism, which is a correctness risk, not circularity. The paper's self-citations ([27], [28]) appear only as background in the related-work discussion and are not load-bearing for the central claim. No uniqueness theorem is imported from the authors, no ansatz is smuggled in via citation, and no known result is merely renamed. Because the only questionable elements are a minor non-load-bearing self-citation and an unproven mechanism assumption, the circularity score is 2.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central mechanism rests on two unproven assumptions: self-supervision loss is a proxy for noise-distribution matching, and the pixel-wise offset can represent the deviation. The only direct evidence is a qualitative histogram on synthetic noise and small PSNR gains. No invented entities; the method uses a per-image offset parameter rather than a new physical entity.

free parameters (2)

Per-method learning rates = LAN: 5e-4, full-trainable: 5e-6, first-layer: 5e-4, last-layer: 1e-4, meta-learning: 1e-5
Hand-tuned 'for stable convergence' separately per method (Section 4.1); no shared budget or sensitivity analysis, so relative gains could be influenced by tuning.
Adaptation iterations = 5, 10, 20
Results are reported at these fixed numbers; no criterion for early stopping or selection is given, and performance can vary with iteration count (Figure 5).

assumptions (3)

domain assumption Noise pixels are independent and clean image pixels are locally correlated, so two transformed views of a single noisy image form a valid self-supervised training pair.
Standard assumption in ZS-N2N and Neighbor2Neighbor, used in Eq. 4 and Eq. 12; inherited from cited methods.
ad hoc to paper Minimizing the self-supervised loss of a frozen denoiser moves input noise toward the distribution the denoiser was trained on.
Stated in Section 3.2 without proof; the paper's mechanism depends on it, and only qualitative histogram evidence is provided (Figure 6).
domain assumption The unseen noise eu can be written as eu = es + epsilon for some training noise es, making the deviation epsilon a meaningful quantity to remove.
Equation 5 is a definition, but epsilon is non-unique because es is arbitrary; the framing assumes the deviation is the right target to learn.

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Cite this review

Pith. "Pith review of LAN: Learning to Adapt Noise for Image Denoising." pith.science (2026). https://pith.science/paper/FEMTVZKW

@misc{pith2026241210651,
  author       = {Pith},
  title        = {Pith review of: LAN: Learning to Adapt Noise for Image Denoising},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FEMTVZKW}},
  note         = {Machine review of arXiv:2412.10651}
}

read the original abstract

Removing noise from images, a.k.a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image denoising with the emergence of advanced deep learning architectures and real-world datasets, recent denoising networks struggle to maintain performance on images with noise that has not been seen during training. One typical approach to address the challenge would be to adapt a denoising network to new noise distribution. Instead, in this work, we shift our focus to adapting the input noise itself, rather than adapting a network. Thus, we keep a pretrained network frozen, and adapt an input noise to capture the fine-grained deviations. As such, we propose a new denoising algorithm, dubbed Learning-to-Adapt-Noise (LAN), where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle. Consequently, the proposed framework exhibits performance improvement on images with unseen noise, displaying the potential of the proposed research direction. The code is available at https://github.com/chjinny/LAN

Figures

Figures reproduced from arXiv: 2412.10651 by the authors.

**Figure 1.** Overview of the motivation of our framework, Learning-to-Adapt-Noise (LAN). Instead of adapting a denoising network to unseen noise, LAN adapts the input noise itself by directly learning to offset the deviations between the unseen noise and the noise distribution a denoising network is trained on. images and corresponding noisy images that are synthesized by adding noise to clean images. Under such formulation, … view at source ↗

**Figure 2.** Overview of conventional methods and our framework, Learning-to-Adapt-Noise (LAN, ours). (a) Pretraining of a denoising network is done with pairs of noisy-clean images, with standard L2 loss. (b) Fine-tuning of a whole denoising network is done with only a given noisy image via self-supervision loss function, such as ZS-N2N [35], to handle unseen noise in the image. (c) Learning-toAdapt-Noise (LAN, ours) is simil… view at source ↗

**Figure 3.** Qualitative comparisons among different adaptation methods. Images are obtained with SIDD-pretrained Uformer. Full-trainable [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Visualization of synthetic noisy images. Noisy image with train noise is a noisy image that is used for pretraining a denoising [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Plot of performance in PSNR over the number of adap [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Histogram of synthetic noise distributions. Adapted [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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