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REVIEW 4 major objections 5 minor 1 cited by

Residual Transformer Fusion Network for Salt and Pepper Image Denoising

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Residual Transformer Fusion Network (RTF-Net) combines a residual-block noise suppressor with a convolutional vision transformer enhancer and reports higher PSNR than six existing salt-and-pepper denoisers in 11 of 12 tested cases.

desk verdict A plausible two-stage ResNet/CvT denoiser whose claimed superiority rests on an unverifiable and internally suspicious BSD300 comparison table. read the letter →

arxiv 2502.09000 v1 pith:PIVBWV5I submitted 2025-02-13 cs.CV cs.LG

Bintang Pradana Erlangga Putra , Heri Prasetyo , Esti Suryani This is my paper · ORCID
classification cs.CVcs.LG
keywords imagedenoisingsalt-and-peppernoiseResidualTransformerFusionNetworkConvolutionalVisionPSNRcomparisonSuppressionStructureEnhancement
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 proposes RTF-Net, a two-stage neural architecture for removing salt-and-pepper noise from images without needing prior knowledge of the noise model or level. The first stage, a Noise Suppression Network built from residual blocks, estimates a noise map that is subtracted from the noisy input. The second stage, a Structure Enhancement Network built from a Convolutional Vision Transformer, adds back the fine details lost in the first stage. The authors report that RTF-Net achieves higher PSNR than six existing salt-and-pepper denoisers on Lena, Bridge, Pepper, and BSD300 test images at 30, 50, and 70 percent noise, with one exception: on Pepper at 30 percent noise, NLSF-CNN scores 32.99 dB versus 31.70 dB for the proposed method.

What carries the argument

The central identity is the two-stage fusion $$I^* = y + \mathrm{SEN}(y; \$\theta$), \quad y = \tilde{I} - \mathrm{NSN}(\tilde{I}; \$\theta$),$$ in which the residual-block branch learns a noise map to subtract and the Convolutional Vision Transformer (CvT) branch learns structural detail to add back. The attention inside the CvT uses pixel unshuffle to cut spatial size, then pixel shuffle and projection afterward, so the transformer can attend globally at reduced computational cost.

What would settle it

Regenerate salt-and-pepper noise on the same test images with the paper's own noise model and rerun the six baseline methods and RTF-Net under identical random seeds; if the baseline PSNR values differ from Table II by more than rounding, or if any baseline matches or beats RTF-Net outside the reported Pepper-30% case on the regenerated noise, the central claim fails.

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Extended reading notes

Core claim

The central claim is that combining a residual-block noise suppression network with a convolutional vision transformer enhancement network yields a salt-and-pepper denoiser that outperforms classical decision-based, non-local, and learned baselines across nearly all tested cases. The paper asserts that the residual branch learns the noise map directly from the noisy image, and the transformer branch restores structural detail after subtraction, so no noise prior is required. On the reported PSNR table, the proposed method exceeds the best baseline by roughly 3 to 14 dB depending on image and noise level, with the single Pepper-30% exception.

Load-bearing premise

The reported PSNR table is only meaningful if all compared methods were evaluated on the same noisy inputs under the same protocol; the paper does not state the noise generation or averaging details, so a mismatch in that protocol would invalidate the claimed superiority.

Editorial extensions

If this is right

  • RTF-Net needs no noise-level prior at test time, so it can be dropped into pipelines where the noise density is unknown.
  • The two-stage design separates noise removal from detail restoration, a pattern that could be reused for other degradation types with the same residual-plus-transformer structure.
  • At 70 percent noise the reported margin over the best baseline is largest, for example 32.85 dB versus 30.18 dB on Lena, suggesting the architecture is especially strong under very heavy corruption.
  • The one reported failure, Pepper at 30 percent noise, is a single-image, single-level case and does not overturn the overall pattern claimed by the paper.

Reading between the lines

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

  • If the reported BSD300 gains, such as 44.56 dB at 30 percent noise, are reproducible, they suggest the network is capturing dataset-level structure rather than only impulse statistics; that should be tested by cross-dataset generalization, which the paper does not report.
  • The same residual-plus-transformer fusion could be applied to random-valued impulse noise or mixed noise with minimal modification, since neither branch is tied to the specific salt-and-pepper model.
  • Because training used DIV2K and testing used classic images, a held-out modern dataset would clarify whether the advantage survives distribution shift.
  • The pixel-unshuffle attention could be compared directly against full-resolution attention to isolate how much of the gain comes from the transformer versus the residual branch.
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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 / 5 minor

Summary. The manuscript proposes RTF-Net, a two-stage network for blind salt-and-pepper image denoising. The first stage, a ResNet-based Noise Suppression Network, estimates a noise map that is subtracted from the noisy input, and the second stage, a Convolutional Vision Transformer based Structure Enhancement Network, adds back structural details. The model is trained on DIV2K patches at 30%, 50%, and 70% noise and evaluated on Lena, Bridge, Pepper, and BSD300. The paper's central claim is that RTF-Net outperforms DBA, NASNLM, PARIGI, NLSF, NLSF-MLP, and NLSF-CNN in all tested configurations except Pepper at 30% noise, where NLSF-CNN is 1.29 dB better.

Significance. RTF-Net is a straightforward combination of two established components: residual CNN blocks for noise-map learning and CvT blocks for structural enhancement. If the reported gains were reproducible under a common protocol, the method would be a modest but useful engineering contribution to impulse-noise removal. The paper provides explicit architecture equations, training curves, and a clear noise model. However, the claimed superiority is currently supported only by single-run PSNR numbers with no ablations, no statistical analysis, no code, and no evaluation protocol. The BSD300 entries in Table II are internally implausible, and the comparison therefore does not yet establish the central claim.

major comments (4)
  1. [Section III.D / Table II] The BSD300 rows are the load-bearing part of the central claim, but the paper gives no aggregation protocol. It does not state whether the reported 44.56/38.03/34.96 dB values are means over all 300 images, medians, or per-image selections, nor whether BSD300 was converted to grayscale or processed per color channel. A mean over a diverse 300-image set being 13-15 dB above the best baseline and 5-6 dB above the method's own Lena results at the same noise levels is internally implausible. The PARIGI entries (12.04/6.01/5.42 dB) fall below the PSNR of a 50% or 70% salt-and-pepper corrupted image, indicating a protocol or transcription mismatch. These rows must be recomputed and described before the claimed superiority is credible.
  2. [Section III.A-C and III.D] There is a large unexplained gap between the reported validation PSNR and the test PSNR at the same noise level. For 30% noise, Figure 7 reports a final validation PSNR of 32.689 dB, while Table II reports 38.87 dB on Lena and 44.56 dB on BSD300. The authors do not explain why the model would generalize 6-12 dB above its DIV2K validation average; this gap further undermines confidence in the Table II entries.
  3. [Section II.B / III.D] The contribution of the proposed fusion is not isolated. No ablation removes the SEN stage, the NSN stage, or replaces the CvT with a plain transformer or the ResNet with a plain CNN. The closest architectural predecessor, the authors' earlier CvT denoiser [7], is cited but not included as a baseline. Without such comparisons, the paper cannot support the claim that the specific NSN+SEN combination is responsible for the reported improvement.
  4. [Section III.D] All reported PSNR values are single deterministic-looking numbers with no random seeds, no repeated trials, and no error bars. Some reported margins are small, such as the 0.24 dB difference on Pepper at 50% noise (30.47 vs 30.23), and the single exception on Pepper at 30% noise could easily change with a different noise realization. The 'excels in all cases except one' claim therefore cannot be statistically assessed from the data presented.
minor comments (5)
  1. [Table I] The hyperparameter table lists 'NSN Features' twice; the second occurrence should presumably read 'SEN Features'. The duplicate row makes the architecture specification ambiguous.
  2. [Section II.B / IV] There are several typographical and language issues: 'RT-Net' should be 'RTF-Net', 'y mage' should be 'y image', and the Indonesian 'dan' appears in the Conclusions ('NLSF-CNN [13] dan NLSF-CNN [13]').
  3. [Section III.E / Figures 12-14] The discussion of deblurring, JPEG artifact removal, and super-resolution uses different images (Airplane, Parrot, Girl) and is not part of the salt-and-pepper denoising evaluation. The connection to the central claim is unexplained, and the captions use verbs like 'deblurred' and 'artifact removed' that do not match the denoising task. This material should be removed or explicitly framed as unrelated pilot experiments.
  4. [Section III.D / Figures 15-17] The figure captions are confusing: subfigures (c)-(e) show NSN outputs at 30%, 50%, and 70% noise, then (f)-(g) show noisy inputs at 50% and 70%, out of sequence. Reordering the panels or clarifying the intermediate outputs would help the reader.
  5. [General] No code, trained model, or data-generation script is provided. Given that the comparison depends on exact noise realizations, releasing the evaluation code with fixed seeds is important for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: RTF-Net's PSNR claims are empirical benchmark measurements, not fitted or self-referential derivations.

full rationale

The paper's central claim (superior PSNR on Lena, Bridge, Pepper, and BSD300 at 30/50/70% salt-and-pepper noise) is an empirical comparison. The model is trained on DIV2K and then tested on the four test sets; no equation in Section II fits a parameter to the reported comparison values, and no Table II entry is a restatement of a training input. Equations (2)-(11) define a standard residual CvT architecture and do not encode the claimed outcome. The only self-citation is [7], the authors' earlier AWGN CvT denoiser, used to motivate prior-free denoising; it is not load-bearing for the numerical superiority claim. The suspicious BSD300 numbers (e.g., PARIGI 12.04 dB at 30% noise, or RTF-Net 44.56 dB vs. 38.87 dB on Lena) indicate a possible protocol or transcription problem, but a measurement/protocol flaw is a correctness issue, not circularity: the claim does not reduce to its inputs by construction. Consequently the circularity score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper's central claim relies on the availability of a good training distribution, the validity of the noise model, and the comparability of literature PSNR values. No new physical or mathematical postulates are introduced; the architecture reuses published components. Hyperparameters in Table I are manual choices without an ablation, and the evaluation protocol is under-specified.

free parameters (5)
  • Patch size = 64x64
    Manual choice, no ablation reported (Table I).
  • NSN depth = 8
    Manual choice, no ablation reported (Table I).
  • SEN depth = 2
    Manual choice, no ablation reported (Table I).
  • Learning rate = 0.001
    Manual choice, no schedule analysis reported (Table I).
  • Number of epochs = 25
    Manual choice; the statement '40,000 iterations separated into 25 epochs' is ambiguous without patch count details.
assumptions (5)
  • domain assumption Salt-and-pepper noise follows the model in Eq. (1) with independent random clipping to 0 or 255.
    Used to generate noisy training and test images; standard but not validated against real impulse noise.
  • domain assumption Residual learning can estimate the noise map via a deep network (He et al. [8]).
    Basis of the NSN; not proven in this paper.
  • domain assumption The Convolutional Vision Transformer (Wu et al. [6]) provides effective global feature extraction for image restoration.
    Basis of the SEN; taken from prior work.
  • domain assumption The DIV2K Training Set is a suitable distribution for learning natural image priors for salt-and-pepper denoising.
    No domain-shift analysis is given.
  • domain assumption PSNR is the appropriate quality metric for comparing denoising methods.
    No perceptual or task-based metrics are used.

how reviews work

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Cite this review

Pith. "Pith review of Residual Transformer Fusion Network for Salt and Pepper Image Denoising." pith.science (2026). https://pith.science/paper/PIVBWV5I

@misc{pith2026250209000,
  author       = {Pith},
  title        = {Pith review of: Residual Transformer Fusion Network for Salt and Pepper Image Denoising},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PIVBWV5I}},
  note         = {Machine review of arXiv:2502.09000}
}
read the original abstract

Convolutional Neural Network (CNN) has been widely used in unstructured datasets, one of which is image denoising. Image denoising is a noisy image reconstruction process that aims to reduce additional noise that occurs from the noisy image with various strategies. Image denoising has a problem, namely that some image denoising methods require some prior knowledge of information about noise. To overcome this problem, a combined architecture of Convolutional Vision Transformer (CvT) and Residual Networks (ResNet) is used which is called the Residual Transformer Fusion Network (RTF-Net). In general, the process in this architecture can be divided into two parts, Noise Suppression Network (NSN) and Structure Enhancement Network (SEN). Residual Block is used in the Noise Suppression Network and is used to learn the noise map in the image, while the CvT is used in the Structure Enhancement Network and is used to learn the details that need to be added to the image processed by the Noise Suppression Network. The model was trained using the DIV2K Training Set dataset, and validation using the DIV2K Validation Set. After doing the training, the model was tested using Lena, Bridge, Pepper, and BSD300 images with noise levels ranging from 30%, 50%, and 70% and the PSNR results were compared with the DBA, NASNLM, PARIGI, NLSF, NLSF-MLP and NLSF-CNN methods. The test results show that the proposed method is superior in all cases except for Pepper's image with a noise level of 30%, where NLSF-CNN is superior with a PSNR value of 32.99 dB, while the proposed method gets a PSNR value of 31.70 dB.

Figures

Figures reproduced from arXiv: 2502.09000 by the authors.

Figure 1
Figure 1. RTF-Net Architecture p ∈ (0, 1) as the noise level, r1 and r2 as two pseudo-random values generated at each pixel in the image to be affixed with salt and pepper noise. B. Residual Transformer Fusion Network In this research, an architecture called Residual Transformer Fusion Network (RTF-Net) is proposed and shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Residual Block Architecture residual block consists of two convolution blocks, where the convolution block consists of a convolution layer, a normalization function layer, and a ReLU activation function layer. Incoming features will go through layers in the convolution block and then the features that do not pass through the convolution block are summed with the features that pass through the convolution block (iden… view at source ↗
Figure 3
Figure 3. CvT Architecture D. Convolutional Vision Transformer The Convolutional Vision Transformer (CvT) [6] depicted in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Transformer Architecture [PITH_FULL_IMAGE:figures/full_fig_p003_4.png]
Figure 6
Figure 6. Figure 6: Training and Validation Loss Graph with 30% Noise Level [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 7
Figure 7. Figure 7: Training and Validation PSNR Graph with 30% Noise Level [PITH_FULL_IMAGE:figures/full_fig_p004_7.png]
Figure 8
Figure 8. Figure 8: Training and Validation Loss Graph with 50% Noise Level [PITH_FULL_IMAGE:figures/full_fig_p005_8.png]
Figure 9
Figure 9. Figure 9: Training and Validation PSNR Graph with 50% Noise Level [PITH_FULL_IMAGE:figures/full_fig_p005_9.png]
Figure 11
Figure 11. Figure 11: Training and Validation PSNR Graph with 70% Noise Level [PITH_FULL_IMAGE:figures/full_fig_p005_11.png]
Figure 12
Figure 12. Figure 12: Visual investigation of deblurred ”Airplane” image using the [PITH_FULL_IMAGE:figures/full_fig_p006_12.png]
Figure 13
Figure 13. Figure 13: Visual investigation of JPEG artifact removed ”Parrot” image using [PITH_FULL_IMAGE:figures/full_fig_p006_13.png]
Figure 15
Figure 15. Figure 15: Visual investigation of denoised ”Lena” image from the proposed method over various noise levels [PITH_FULL_IMAGE:figures/full_fig_p007_15.png]
Figure 16
Figure 16. Figure 16: Visual investigation of denoised ”Bridge” image from the proposed method over various noise levels [PITH_FULL_IMAGE:figures/full_fig_p008_16.png]
Figure 17
Figure 17. Figure 17: Visual investigation of denoised ”Pepper” image from the proposed method over various noise levels [PITH_FULL_IMAGE:figures/full_fig_p008_17.png]

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Reviewed August 7, 2026 · model on record in the stance chip above.