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REVIEW 4 major objections 9 minor 99 references

Lower resolution can yield sharper MRI scans, physics-aware model shows

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A 2D Gaussian Splatting framework with MRI-specific anatomical priors, physics-constrained intensity modeling, and meta-learning domain adaptation achieves state-of-the-art MRI super-resolution while showing that intermediate input resolutions can outperform maximum-resolution inputs.

T0 review reviewed 2026-07-08 challenge →

load-bearing objection Adapting 2D Gaussian Splatting to MRI with physics-constrained signal modeling is a genuine methodological contribution. The 'optimal resolution isn't the highest' headline claim is not supported by the paper's own real data. the 4 major comments →

arxiv 2607.06238 v1 pith:OX5W2KRA submitted 2026-07-07 cs.CV

PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution

Lihua Wei , Huatong Gao , Jia Gong , Zhiyu Tan , Hao Li , Jun Liu , Zhihua Ren This is my paper
classification cs.CV PACS 87.61.-c87.57.N-
keywords MRI super-resolutionGaussian Splattingphysics-aware reconstructionresolution-SNR trade-offultra-low-field MRImeta-learningdynamic resolution
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 the starting image fed into an MRI super-resolution pipeline need not be the highest-resolution scan available. Because MRI physics couples spatial resolution to signal-to-noise ratio (SNR) — smaller voxels mean proportionally less signal per voxel while noise stays fixed — pushing for maximum resolution at acquisition time can actually produce a worse input for downstream enhancement. The authors demonstrate this with non-monotonic quality curves across resolution scales on both simulated and real data, showing that an intermediate resolution (around 0.7× on simulated data, 0.76× on real data) yields the best final reconstruction. To operationalize this insight, they adapt 2D Gaussian Splatting — a technique that represents images as collections of parametric geometric primitives rather than pixel grids — to MRI, making it resolution-agnostic so it can accept inputs at any scale. They augment it with three MRI-specific components: anatomically-guided placement of Gaussian primitives (denser in structurally complex tissue like cortical gray matter), a covariance dictionary that constrains primitive shapes to MRI-realistic configurations, and a physics-constrained intensity model that predicts proton density and relaxation parameters rather than directly regressing pixel values. A meta-learning pipeline bridges the gap between simulated training data and real ultra-low-field (64 mT) scanner acquisitions. On the FastMRI benchmark the method achieves 34.26 dB PSNR at 4× upscaling, exceeding the prior best by 1.45 dB, and on real 64 mT–3T paired data it reaches 26.85 dB versus under 20.3 dB for baselines.

Core claim

The central discovery is empirical: the relationship between input resolution and super-resolution output quality is non-monotonic, with an intermediate resolution-SNR operating point producing the best reconstructions. This finding rests on the well-established MRI physics that SNR scales linearly with voxel volume while noise is resolution-independent, creating a regime where excessively fine resolution at acquisition time destroys more signal information than the extra spatial samples provide. The paper's methodological contribution is a 2D Gaussian Splatting framework adapted to MRI that can accept inputs at arbitrary resolutions and exploits this non-monotonicity, using physics-constrae

What carries the argument

2D Gaussian Splatting adapted to MRI with three innovations: (1) segmentation-guided primitive initialization that distributes Gaussian kernels according to tissue complexity (gray matter gets more primitives than CSF), (2) an MRI-specific covariance dictionary of 1,001 kernel shapes learned from real high-field MRI data rather than natural images, and (3) a physics-constrained signal model that computes pixel intensity as c_i = ρ_i · e^{-R2,i} + δ_i from predicted proton density ρ and effective relaxation rate R2, rather than directly regressing intensities. A first-order MAML meta-learning pipeline trains on simulated data with episodic real-data injection for domain adaptation.

Load-bearing premise

The physics-constrained signal model includes an unconstrained learnable residual term that is added to the physics-based intensity formula. This residual can in principle absorb any deviation from the idealized signal equation, meaning the network could learn to bypass the physics constraint entirely and regress target intensities through the residual, making the biophysical-plausibility guarantee depend on whether the network actually uses the physics pathway rather than a兜

What would settle it

On real 64 mT–3T paired data, the method achieves 26.85 dB PSNR and 0.8856 SSIM, outperforming all baselines (LIIF, LTE, Pixel-to-Gaussian) by over 6.5 dB in PSNR. On the FastMRI benchmark, it achieves 34.26 dB PSNR at 4× upscaling, exceeding the prior best method (MS-PRDDiff) by 1.45 dB. The non-monotonic resolution hypothesis is supported by non-monotonic PSNR/SSIM curves across input resolution scales on both simulated (best at ×0.7) and real (competitive at ×0.76) datasets.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the non-monotonic resolution-SNR finding generalizes, MRI acquisition protocols could be redesigned to capture at an intermediate resolution that is cheaper and faster to acquire, rather than maximally resolved scans, trusting post-processing to recover the detail.
  • The resolution-agnostic Gaussian Splatting representation could be extended to 3D volumetric MRI, where the SNR penalty for isotropic resolution doubling is a factor of 8 (64× more averages to recover), making the optimal-resolution argument even stronger.
  • The physics-constrained intensity decomposition into proton density and relaxation parameters could enable the super-resolution model to output quantitative tissue parameter maps as a byproduct, not just enhanced images.
  • If the intermediate-resolution optimum is confirmed across more pulse sequences and field strengths, scanner manufacturers could build acquisition protocols that intentionally target the SNR-optimal point rather than the finest nominal resolution.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 9 minor

Summary. This paper proposes PhyMRI-SR, a physics-aware MRI super-resolution framework based on 2D Gaussian Splatting. The central thesis is that the optimal input resolution for MRI super-resolution is not necessarily the highest achievable resolution, due to the resolution-SNR trade-off inherent in MRI physics. The framework introduces three innovations: (1) a prior-aware Gaussian representation with anatomical structure-guided initialization and an MRI-specific covariance dictionary, (2) physics-constrained signal modeling that predicts proton density and relaxation parameters rather than directly regressing intensities, and (3) a meta-learning framework for domain adaptation from simulated to real data. Experiments are conducted on simulated IXI data, a real 3T-5T dynamic-resolution dataset, the FastMRI benchmark, and a real 64mT-3T paired dataset. The method reports state-of-the-art performance on FastMRI (34.26 dB PSNR at 4x) and substantial improvements on real 64mT-3T data (26.85 dB vs. <20.3 dB for baselines).

Significance. The paper addresses a clinically relevant problem and proposes a well-motivated framework. The resolution-SNR derivation (Eqs. 1-4, S17-S36) is standard but correctly applied and clearly presented. The adaptation of 2D Gaussian Splatting to MRI with domain-specific priors is a reasonable architectural choice for continuous-scale super-resolution. The FastMRI benchmark results (Table II) are strong, with a 1.45 dB PSNR improvement over the best diffusion-based baseline at 4x. The real 64mT-3T results (Table III) show a substantial 6.5+ dB improvement over baselines, which is noteworthy. The meta-learning domain adaptation strategy is a practical contribution for the paired-data scarcity problem. However, the significance of the central 'optimal resolution' claim is weakened by inconsistencies in the supporting evidence and by the unconstrained residual in the physics model.

major comments (4)
  1. §IV-B, Eq. (16): The physics-constrained signal model c_i = rho_i * e^{-R2,i} + delta_i includes an unconstrained learnable residual delta_i. Since delta_i can absorb any deviation from the idealized signal equation, the claim of 'biophysically plausible contrast' is at risk of being vacuous — the network can learn delta_i ≈ c_i - rho_i * e^{-R2,i} for any target intensity, reducing the physics constraint to a reparameterization of direct intensity regression. The ablation in Table V shows the physics module improves SSIM from 0.55 to 0.85, but without constraining or analyzing delta_i (e.g., reporting its magnitude relative to the physics term, or adding a regularization term), it is unclear whether the improvement comes from the physics constraint or from the reparameterization acting as a different optimization landscape. The authors should either constrain delta_i (e.g., norm-bounded
  2. §V-C.2 and Table I (Real Multi-Resolution Dataset): The headline claim that 'the optimal resolution for MRI super-resolution is not necessarily the highest achievable resolution' is contradicted by the paper's own real data. On the 3T-5T dataset, the highest input resolution (x1.04) achieves the best PSNR (26.78 dB) and tied-best SSIM (0.8775). The paper acknowledges this but reframes it by pointing to secondary metrics (HFEN best at x0.76, DISTS best at x0.83). However, the claim is stated in terms of overall quality, and the primary metrics favor the highest resolution on real data. On the simulated IXI data, the evidence is marginal: x0.7 gives PSNR 28.10 vs. 28.06 at x1.0, a 0.04 dB difference within typical run-to-run variance. The authors should either soften the headline claim to reflect that different quality metrics favor different resolutions, or provide statistical evidence (e
  3. §S2.11 (Eqs. S37-S41) and Table I: The degradation model for simulating low-field MRI uses task-dependent blur sigma and noise alpha parameters, but the paper does not specify how these parameters are calibrated to the resolution-SNR trade-off in Eq. (4) for each resolution scale. If the noise level at each resolution scale is not set proportional to voxel volume (as Eq. 4 dictates), the 'optimal' intermediate resolution observed on simulated data could be an artifact of the chosen sigma and alpha values rather than a fundamental physics property. The authors should explicitly state the sigma and alpha values used for each resolution scale in Table I and verify that they are consistent with the SNR scaling predicted by Eq. (4).
  4. Table VI: The ablation table for meta-learning labels the second variant 'w/o Physics' in the Method column, but the text describes it as 'w/o Meta' (a model without meta-learning). This appears to be a labeling error. If the variant truly removes meta-learning, the label should read 'w/o Meta'. If it removes physics, then the ablation does not test the meta-learning contribution. This needs correction and clarification.
minor comments (9)
  1. Abstract: 'futher' should be 'further' (appears twice in abstract and introduction).
  2. §IV-A: 'domian-specific' should be 'domain-specific'.
  3. §IV-A.2: The covariance dictionary is built from 4,241 5T MRI slices (§S3.A). The paper should clarify whether these slices overlap with the test set used in Table I (Real Multi-Resolution Dataset), and if so, whether this constitutes a form of train-test leakage. If the dictionary is built from training data only, this should be stated explicitly.
  4. §V-A: 'acquision' should be 'acquisition' (appears in Eq. 3 caption area).
  5. Table I: The simulated IXI dataset shows DISTS values of 0.1231 for both x1.0 and x0.9, which is suspicious. The authors should verify this is not a copy error.
  6. §V-C.2: The phrase 'the optimal resolution lies in an intermediate range rather than at the extreme ends' overstates the real-data evidence, where x1.04 is best or tied-best on two of four metrics. Consider softening this language.
  7. Figure 2 caption: 'segmentator' is non-standard terminology; consider 'segmentation network' or 'segmentation model' for consistency with standard terminology.
  8. §S3.C.3, Eq. (S42): The reference 'Method Section ??' is an unresolved LaTeX cross-reference.
  9. The paper would benefit from reporting standard deviations or confidence intervals for the dynamic-resolution experiments (Table I), especially given that the key PSNR differences on simulated data (28.10 vs. 28.06) are small.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for a careful and constructive review. The referee raises four major points: (1) the unconstrained residual delta_i in the physics-constrained signal model may render the physics constraint vacuous; (2) the headline claim that optimal resolution is not the highest is contradicted by the real 3T-5T data where the highest resolution achieves best PSNR; (3) the degradation model's sigma and alpha parameters may not be calibrated to the SNR scaling of Eq. (4), potentially making the simulated optimal-resolution finding an artifact; and (4) a labeling error in Table VI. We address each point below and describe the revisions we will make.

read point-by-point responses

  1. Referee: §IV-B, Eq. (16): The physics-constrained signal model c_i = rho_i * e^{-R2,i} + delta_i includes an unconstrained learnable residual delta_i. Since delta_i can absorb any deviation from the idealized signal equation, the claim of 'biophysically plausible contrast' is at risk of being vacuous. The ablation in Table V shows the physics module improves SSIM from 0.55 to 0.85, but without constraining or analyzing delta_i, it is unclear whether the improvement comes from the physics constraint or from the reparameterization acting as a different optimization landscape.

    Authors: The referee raises a valid and important concern. We agree that without any constraint or analysis on delta_i, the physics-constrained formulation could in principle degenerate into a reparameterization of direct intensity regression, which would weaken our claim of biophysically plausible contrast. In the revised manuscript, we will make two changes. First, we will add an L2 regularization term on delta_i (lambda * ||delta||_2) to the training objective, penalizing large deviations from the physics-predicted signal and encouraging the network to rely on the physical model rather than absorbing all intensity information into the residual. Second, we will report the ratio ||delta_i|| / ||rho_i * e^{-R2,i}|| measured on the test set, demonstrating that the physics term dominates the residual and that delta_i accounts for a small fraction of the total intensity. This will directly address whether the improvement in SSIM (0.55 to 0.85) comes from the physics constraint rather than from reparameterization alone. We will also add an ablation variant with delta_i = 0 (fully constrained) to isolate the contribution of the physics term from the residual. We note that the qualitative results in Figure S8 already provide indirect evidence that the physics module restores correct grayscale fidelity consistent with the ground truth, which pure reparameterization would not necessarily achieve. However, we agree that quantitative analysis of delta_i is needed to substantiate this claim. revision: yes

  2. Referee: §V-C.2 and Table I: The headline claim that 'the optimal resolution for MRI super-resolution is not necessarily the highest achievable resolution' is contradicted by the paper's own real data. On the 3T-5T dataset, the highest input resolution (x1.04) achieves the best PSNR (26.78 dB) and tied-best SSIM (0.8775). The paper acknowledges this but reframes it by pointing to secondary metrics (HFEN best at x0.76, DISTS best at x0.83). On the simulated IXI data, the evidence is marginal: x0.7 gives PSNR 28.10 vs. 28.06 at x1.0, a 0.04 dB difference within typical run-to-run variance.

    Authors: The referee is correct that the real 3T-5T data does not unambiguously support the headline claim when PSNR and SSIM are the primary metrics. On the real data, the highest resolution (x1.04) achieves the best PSNR (26.78 dB) and tied-best SSIM (0.8775), while intermediate resolutions are favored only by HFEN (x0.76) and DISTS (x0.83). On the simulated IXI data, the PSNR difference between x0.7 (28.10 dB) and x1.0 (28.06 dB) is indeed marginal at 0.04 dB. We will revise the manuscript in two ways. First, we will soften the headline claim from 'the optimal resolution for MRI super-resolution is not necessarily the highest achievable resolution' to a more precise statement: 'different quality metrics favor different resolution settings, and intermediate resolutions can achieve competitive or superior performance on perceptual and high-frequency metrics, suggesting that the highest input resolution is not universally optimal across all aspects of image quality.' Second, we will run multiple random seeds (at least 3) on the simulated IXI experiment and report mean and standard deviation for each resolution scale, so that the reader can assess whether the 0.04 dB difference is within run-to-run variance. If the difference is not statistically significant, we will state this explicitly and frame the simulated result as 'no significant degradation at intermediate resolution' rather than 'intermediate resolution is superior.' We acknowledge that the current evidence does not support a strong universal claim, and the revised framing will reflect this honestly. revision: yes

  3. Referee: §S2.11 (Eqs. S37-S41) and Table I: The degradation model for simulating low-field MRI uses task-dependent blur sigma and noise alpha parameters, but the paper does not specify how these parameters are calibrated to the resolution-SNR trade-off in Eq. (4) for each resolution scale. If the noise level at each resolution scale is not set proportional to voxel volume (as Eq. 4 dictates), the 'optimal' intermediate resolution observed on simulated data could be an artifact of the chosen sigma and alpha values rather than a fundamental physics property.

    Authors: This is a fair concern. The referee is right that if the noise level alpha at each resolution scale is not set consistently with the SNR scaling predicted by Eq. (4) (i.e., SNR proportional to voxel volume), then the observed non-monotonic relationship between input resolution and SR performance on simulated data could be an artifact of the degradation parameter choices rather than a physics-driven phenomenon. In the revised manuscript, we will add a table specifying the exact sigma and alpha values used for each resolution scale in the simulated IXI experiment. We will also verify and explicitly state whether these values are calibrated to be proportional to voxel volume as dictated by Eq. (4). If the current values are not calibrated this way, we will re-run the simulated experiment with properly calibrated noise levels and update Table I accordingly. If the non-monotonic trend disappears under properly calibrated noise, we will report this honestly and revise our claims. We agree that this calibration is essential for the simulated-data evidence to be credible. revision: yes

  4. Referee: Table VI: The ablation table for meta-learning labels the second variant 'w/o Physics' in the Method column, but the text describes it as 'w/o Meta' (a model without meta-learning). This appears to be a labeling error. If the variant truly removes meta-learning, the label should read 'w/o Meta'. If it removes physics, then the ablation does not test the meta-learning contribution.

    Authors: The referee is correct. This is a labeling error in Table VI. The text in the accompanying paragraph clearly describes the variant as 'a model without meta-learning (w/o Meta),' and the purpose of the ablation is to test the contribution of meta-learning. The label in the Method column should read 'w/o Meta,' not 'w/o Physics.' We will correct this in the revised manuscript. We apologize for the confusion. revision: yes

Circularity Check

0 steps flagged

No significant circularity found; one minor concern with covariance dictionary encoding target-domain statistics, but it is a learned prior, not a circular derivation.

full rationale

The paper's derivation chain does not exhibit circularity. The resolution-SNR trade-off (Eqs. 1–4, S17–S36) is standard MRI physics properly cited from external textbooks [33, 34, 73] and derived from the spin-echo signal equation without self-reference. The physics-constrained signal model (Eqs. 13–16) uses the well-known T2-weighted signal equation S ≈ ρ·e^{-R2}; the paper does not claim to derive this equation but adopts it as a known physical relationship. The unconstrained residual δ_i (Eq. 16) raises a correctness concern (it could absorb any deviation, making the physics constraint vacuous), but this is a validity issue, not circularity — the paper does not present δ_i as a prediction or first-principles result. The covariance dictionary (Section IV-A.2) is built by fitting 2D GS to 4,241 high-resolution 5T MRI slices and then constraining predicted covariances to linear combinations of dictionary entries. While this encodes target-domain spatial statistics into the model, it is presented as a learned prior (a dictionary), not as a prediction or derivation. The paper does not claim to 'predict' MRI system characteristics from first principles; it explicitly states the dictionary is constructed empirically from data. No self-citation chain is load-bearing: the 2D GS framework is cited from [39] (Pixel-to-Gaussian, external authors), meta-learning from MAML [74] (external), and segmentation from SynthSeg [95] (external). The 'optimal resolution' hypothesis is an empirical finding from experiments, not a derivation that reduces to its inputs. The minor score of 2 reflects the covariance dictionary's encoding of target-domain statistics, which could limit generalization claims, but this is a methodological design choice rather than a circular derivation.

Axiom & Free-Parameter Ledger

6 free parameters · 4 axioms · 1 invented entities

The paper introduces several hand-set or learned parameters (complexity weights, offset scale, dictionary size, residual term) and relies on domain assumptions about T2-weighted signal simplification, tissue property invariance, and degradation model fidelity. The covariance dictionary is a new entity without independent falsifiable handles.

free parameters (6)
  • Tissue complexity weights w_k (w_GM, w_WM, w_CSF) = w_GM > w_WM > w_CSF (exact values not stated)
    Hand-set weights controlling primitive density per tissue type in Eq. 9. Not fitted to data but chosen by anatomical intuition.
  • Position offset scale δ = Not specified
    Controls maximum displacement range in Eq. 11. Stated as a scaling factor but exact value not given.
  • Covariance dictionary size M = 1001
    Number of dictionary entries built from 4,241 5T MRI slices (Section S3.A). 10 values per parameter × 3 parameters + zero kernel.
  • Learnable residual δ_i = Learned per-primitive
    Eq. 16. Unconstrained residual absorbing physics model deviations. Effectively a free parameter per Gaussian primitive.
  • Meta-learning inner-loop rate α = Not specified
    Eq. S42. Inner-loop learning rate for task adaptation.
  • Meta-learning outer-loop rate β = Not specified
    Eq. S43. Outer-loop meta-update learning rate.
axioms (4)
  • domain assumption MRI signal for T2-weighted spin echo simplifies to S ≈ ρ·e^{-TE/T2} when TR >> T1
    Eq. 14. Standard MRI physics assumption for T2-weighted imaging. Used as the basis for physics-constrained signal modeling.
  • domain assumption Tissue properties ρ and R2 are resolution-invariant
    Section IV-B.2: 'since ρ and R2 reflect intrinsic tissue properties that are independent of imaging resolution.' This motivates the decomposition but is not independently verified in the paper.
  • domain assumption The empirical covariance distribution from 5T MRI generalizes to other field strengths
    Section IV-A.2. The covariance dictionary is built from 5T data but applied to 64mT and 3T super-resolution. No validation that 5T covariance statistics transfer.
  • domain assumption Simulated degradation model (Eq. S37) accurately represents real low-field MRI
    Section S2.11. The degradation model combines PSF blurring, Rician noise, and bias field. Used to generate all simulated training data and to support the dynamic-resolution hypothesis.
invented entities (1)
  • MRI-Specific Covariance Dictionary no independent evidence
    purpose: Constrains Gaussian primitive covariance to MRI-realistic configurations
    Built from 5T MRI data (Section S3.A). No falsifiable prediction outside the paper — it is a precomputed lookup table from training-domain statistics. Whether it generalizes to unseen field strengths or pathologies is untested.

reviewed 2026-07-08 · how reviews work

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Cite this review

Pith. "Pith review of PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution." pith.science (2026). https://pith.science/paper/OX5W2KRA

@misc{pith2026260706238,
  author       = {Pith},
  title        = {Pith review of: PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OX5W2KRA}},
  note         = {Machine review of arXiv:2607.06238}
}
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read the original abstract

Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs. We rethink MRI super-resolution as a physics-aware reconstruction problem, in which the goal is to identify the optimal resolution-SNR configuration and then super-resolve it to obtain high-quality MRI results. A key implication of this formulation is that MRI resolution becomes dynamic rather than fixed. To handle such resolution-heterogeneous inputs, we adapt 2D Gaussian Splatting (2D GS) to MRI by formulating reconstruction as a coordinate-based, resolution-agnostic rendering problem. To further enhance fidelity, we introduce three innovations: (1) a prior-aware Gaussian representation that combines an Anatomical Structure Prior for tissue-specific kernel initialization with an Imaging System Prior that captures hardware characteristics via a covariance dictionary; (2) a physics-constrained signal modeling scheme that predicts intrinsic tissue parameters (proton density rho and effective relaxation rate R2) and synthesizes intensities through governing physical equations, ensuring biophysically plausible contrast; and (3) a meta-learning framework that alleviates paired-data scarcity by pretraining on simulated data and adapting to real-world conditions. Extensive experiments on dynamic-resolution datasets and standard benchmarks demonstrate that our method achieves state-of-the-art performance, highlighting its strong potential for clinical deployment.

Figures

Figures reproduced from arXiv: 2607.06238 by Hao Li, Huatong Gao, Jia Gong, Jun Liu, Lihua Wei, Zhihua Ren, Zhiyu Tan.

Figure 1
Figure 1. Figure 1: Illustration of the trade-off between spatial resolution and signal [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 1
Figure 1. Figure 1: This underscores that the given low-resolution image [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed physics-aware 2D GS framework. An arbitrary-resolution input is processed through two parallel pathways: a segmentator [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Effect of input resolution on super-resolution quality on the simulated IXI dataset. Two representative cases are shown with varying scales from [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visual comparison on the real 3T–5T dynamic-resolution dataset. Our method produces reconstructions most consistent with the 5T Ground Truth, [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The qualitative results on three simulated datasets under the optimal input resolution. Our method reconstructs fine details of the Ground Truth on [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visual comparison on real 64mT-3T dataset. The SR results of our method can recover fine details from the Ground Truth. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

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