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arxiv: 2606.23200 · v1 · pith:WV73K6MHnew · submitted 2026-06-22 · 📡 eess.IV · cs.CV

NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling

Jaehyun Cho , YoungJoon Yoo This is my paper

Pith reviewed 2026-06-26 06:26 UTC · model grok-4.3

classification 📡 eess.IV cs.CV
keywords self-supervised denoisingmedical image denoisingneighbor-guided samplingCT denoisingMRI denoisingpatch matching
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The pith

NGPS enables self-supervised denoising from misaligned neighboring slices without registration

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

The paper introduces Neighbor-Guided Patch Sampling (NGPS) to construct supervision for denoising from neighboring slices in volumetric medical images despite inter-slice misalignment. Instead of using adjacent slices directly or masking discrepant regions, NGPS uses a simple guide image to search for structurally similar patches in a local neighborhood. It then retrieves the raw noisy values from the neighbor at the matched locations to form pseudo targets. This decoupling allows exploiting more evidence around anatomical boundaries while avoiding the need for a learned registration module. The method shows improvements in fidelity and structure-sensitive metrics on CT and synthetic-Rician MRI data.

Core claim

NGPS constructs local pseudo targets without a learned registration module. For each masked location, it searches a local neighborhood for structurally similar candidate patches using a simple guide image (e.g., fast bilateral filtering), while retrieving the supervision signal directly from the raw noisy neighbor at the matched coordinates.

What carries the argument

Neighbor-Guided Patch Sampling, which matches patches on a noise-attenuated guide image and retrieves raw signals from neighboring slices at matched coordinates.

If this is right

  • Exploits neighboring evidence around high-frequency boundaries that would otherwise be masked.
  • Achieves better fidelity and structure preservation in evaluated CT and MRI settings without registration learning.
  • Provides a lightweight alternative for building supervision under local misalignment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be tested on real clinical datasets with natural misalignments to validate the guide image assumption.
  • It may inspire similar decoupling strategies in other self-supervised tasks involving misaligned data.
  • Potential reduction in model complexity by avoiding registration networks.

Load-bearing premise

A simple non-learned guide image such as fast bilateral filtering is sufficient to reliably locate structurally similar patches despite noise and local misalignment.

What would settle it

A comparison where the guide image matching fails under strong misalignment and leads to lower performance than standard masking methods.

Figures

Figures reproduced from arXiv: 2606.23200 by Jaehyun Cho, Youngjoon Yoo.

Figure 1
Figure 1. Figure 1: Illustration of inter-slice anatomical misalignment and the resulting critical information loss from conventional masking strategies (τ = 0.1) in 2.5 mm LIDC￾IDRI data [2]. (a) The absolute difference map (|n − (n + 1)|) and the masked pixels (highlighted in red). (b) The histogram revealing the severity of this spatial information loss within critical Regions of Interest (ROIs) across the validation set. … view at source ↗
Figure 2
Figure 2. Figure 2: Overall NGPS pipeline. A noisy slice triplet is low-pass filtered to pro￾duce noise-attenuated guide (“Pseudo-Clean”) slices, whose pairwise differences yield direction-aware threshold masks with reduced sensitivity to raw noise. For each masked location, NGPS searches a local window in the adjacent slice for the Top-K similar patches (green); the matched coordinates are then used to retrieve and average t… view at source ↗
Figure 3
Figure 3. Figure 3: Quantitative comparison on IXI dataset [4] (Simulated Rician noise). [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons on quarter-dose and ultra-low-dose CT. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on IXI [4] under 9% synthetic Rician noise. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablations for mask sensitivity and search-window design. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Ablation study on the Top-K ensemble size in the NGPS module [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
read the original abstract

Neighboring-slice self-supervised denoising is attractive for volumetric medical imaging, yet inter-slice misalignment breaks anatomical correspondence and often yields ghosting and blurred margins when adjacent slices are used naively as targets. We propose Neighbor-Guided Patch Sampling (NGPS), a lightweight framework that constructs neighboring supervision under local inter-slice misalignment without explicit registration. To avoid learning from misleading targets, prior methods commonly mask discrepant regions, but this stabilizes training at the cost of leaving a non-trivial portion of neighboring evidence unexploited, particularly around high-frequency anatomical boundaries. NGPS addresses this by decoupling structure matching from signal retrieval: for each masked location, it searches a local neighborhood for structurally similar candidate patches using a simple guide image (e.g., fast bilateral filtering), while retrieving the supervision signal directly from the raw noisy neighbor at the matched coordinates. By matching on a noise-attenuated guide while retrieving raw values from neighboring slices, NGPS constructs local pseudo targets without a learned registration module. Across the evaluated CT and synthetic-Rician MRI settings, NGPS improves fidelity and structure-sensitive metrics. Code is available at https://github.com/cv-cho/NGPS .

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper introduces Neighbor-Guided Patch Sampling (NGPS), a lightweight self-supervised denoising framework for volumetric medical imaging. It addresses inter-slice misalignment by decoupling structure matching (performed on a non-learned guide image such as bilateral filtering) from raw-signal retrieval (taken directly from the noisy neighbor at matched coordinates). This avoids both explicit registration modules and the information loss from masking discrepant regions. The authors claim that NGPS yields improvements in fidelity and structure-sensitive metrics on CT and synthetic-Rician MRI data, with code released publicly.

Significance. If the experimental claims hold, NGPS would provide a practical, registration-free route to exploit neighboring-slice supervision in misaligned clinical volumes. The conceptual separation of guide-based matching from raw-value retrieval is clean and avoids learned components, which could reduce training complexity. Public code availability supports reproducibility and is a positive factor in the assessment.

major comments (1)
  1. [Abstract] Abstract: the central claim that 'NGPS improves fidelity and structure-sensitive metrics' across CT and synthetic-Rician MRI settings is presented without any quantitative results, baseline comparisons, ablation studies, or description of how inter-slice misalignment was simulated. This absence makes it impossible to verify whether the data support the claim and is load-bearing for the paper's contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that the central claim requires supporting quantitative detail to be verifiable from the abstract alone and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'NGPS improves fidelity and structure-sensitive metrics' across CT and synthetic-Rician MRI settings is presented without any quantitative results, baseline comparisons, ablation studies, or description of how inter-slice misalignment was simulated. This absence makes it impossible to verify whether the data support the claim and is load-bearing for the paper's contribution.

    Authors: We agree that the abstract should be self-contained and will revise it to include representative quantitative results (e.g., PSNR/SSIM gains on the evaluated CT and synthetic-Rician MRI datasets), a brief statement of the misalignment simulation protocol, and reference to the main baselines and ablations. These details already appear in Sections 4 and 5 of the manuscript; the revision will simply surface the key numbers and simulation description in the abstract itself. No other changes to the technical contribution are required. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The provided abstract and description present NGPS as a procedural framework that decouples structure matching (via a non-learned guide image) from raw-signal retrieval to construct pseudo-targets. No equations, derivations, fitted parameters, or self-citations are shown that reduce any claimed result to its inputs by construction. The method is described directly in terms of its algorithmic steps without mathematical self-reference or load-bearing prior results from the same authors. This is the expected outcome for a methods paper focused on implementation rather than theoretical derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method implicitly assumes local structural similarity exists in misaligned neighbors.

axioms (1)
  • domain assumption Local neighborhoods around corresponding locations contain structurally similar patches even under inter-slice misalignment.
    This premise enables the search for candidate patches using the guide image.

pith-pipeline@v0.9.1-grok · 5735 in / 1197 out tokens · 28256 ms · 2026-06-26T06:26:13.854066+00:00 · methodology

discussion (0)

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