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arxiv: 2606.21910 · v1 · pith:VDUMQZOAnew · submitted 2026-06-20 · 💻 cs.CV

Fidelity- and Perception-Aware Local Implicit Attention for Arbitrary-Scale Image Super-Resolution

Yu-Syuan Xu , Hao-Lun Sun , Hao-Wei Chen , Hsien-Kai Kuo , Chun-Yi Lee This is my paper

Pith reviewed 2026-06-26 12:24 UTC · model grok-4.3

classification 💻 cs.CV
keywords arbitrary-scale image super-resolutiondiffusion modelsattention mechanismsfidelity and perceptionimage reconstructionFPAMFPSM
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The pith

FPLIA integrates fidelity features into diffusion pipelines for realistic arbitrary-scale super-resolution.

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

Arbitrary-scale image super-resolution must trade off smooth but inaccurate pixel-regression outputs against sharp yet hallucinated diffusion results. The paper proposes FPLIA to insert fidelity-oriented features into the diffusion process through two new modules. FPAM applies self-attention and cross-attention across fidelity and perceptual features to strengthen representation. FPSM then adaptively selects the strongest features for final RGB prediction. Experiments on standard benchmarks indicate the combination improves perceptual realism while preserving reconstruction accuracy.

Core claim

FPLIA is a framework that integrates fidelity-oriented features into a diffusion pipeline for arbitrary-scale image super-resolution by means of the Fidelity and Perception Attention Module, which performs self-attention and cross-attention on fidelity and perceptual features, and the Fidelity and Perception Select Module, which adaptively chooses the most representative features for RGB value prediction, thereby producing reconstructions that are both realistic and faithful.

What carries the argument

Fidelity and Perception Attention Module (FPAM) and Fidelity and Perception Select Module (FPSM), which combine and select between fidelity-oriented and perceptual features via attention mechanisms inside a diffusion pipeline.

If this is right

  • FPLIA produces superior perceptual realism on standard ASISR benchmarks.
  • Reconstruction accuracy measured by pixel-wise metrics is maintained.
  • Risk of structural hallucinations is reduced relative to pure diffusion approaches.
  • Complementary fidelity and perceptual features are exploited for RGB prediction.

Where Pith is reading between the lines

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

  • The same attention-selection pattern could be tested on other continuous-scale restoration problems such as video super-resolution.
  • The modules might be inserted into non-diffusion generative backbones to check whether the fidelity-perception balance transfers.
  • Evaluation on real captured low-resolution images with unknown degradations would test robustness beyond synthetic benchmarks.

Load-bearing premise

The FPAM and FPSM modules integrate into an existing diffusion pipeline without creating new structural inconsistencies or requiring hyperparameter tuning beyond what the paper describes.

What would settle it

Quantitative evaluation on standard ASISR benchmarks in which FPLIA shows no improvement in perceptual metrics such as LPIPS while PSNR and SSIM remain comparable would falsify the central effectiveness claim.

Figures

Figures reproduced from arXiv: 2606.21910 by Chun-Yi Lee, Hao-Lun Sun, Hao-Wei Chen, Hsien-Kai Kuo, Yu-Syuan Xu.

Figure 1
Figure 1. Figure 1: Regression-based and diffusion-based approaches for arbitrary-scale image super￾resolution (ASISR) exhibit complementary failure modes. Regression methods (e.g., LIT [4] with SwinIR [15]) reconstruct structurally faithful images that suppress fine textures, such as the blinds in the first row. Diffusion methods (e.g., Kim et al. [13] with LDM [19]) recover perceptually realistic textures, yet may hallucina… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed FPLIA framework. (a) FPLIA jointly exploits fidelity￾oriented and perceptual features for advancing ASISR. (b) FPAM employs self-attention and bi-directional cross-attention mechanisms to produce enhanced representative latent features. (c) FPSM estimates confidence scores and adaptively selects higher-confidence features for predicting RGB values at each queried coordinate. comple… view at source ↗
Figure 3
Figure 3. Figure 3: Fidelity-oriented metric PSNR versus perceptual metric LPIPS across ASISR methods on DIV2K validation set [1] and Set5 [2] at wide-range upscaling factors. Our method (red circle) achieve the Pareto frontier between PSNR and LPIPS compared to regression-based methods (blue circle) and diffusion methods (green circle). Implementation details. The fidelity-oriented feature extractor Eθ is instantiated with S… view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of the qualitative results of LIIF [5], LTE [14], LIT [4], CiaoSR [3], HIIF [12], IDM [7], Kim et al. [13], and our proposed FPLIA. HIIF -SwinIR KIM et al. Ours LR 1.6× 2.2× 2.8× 3.5× 4.3× [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the qualitative results of HIIF [12], Kim et al. [13], and our proposed FPLIA with non-integer upscaling factors. as its generative process hallucinates textures without structural anchoring. FPLIA recovers the correct pattern with sharp detail, a result attributable to the bidirectional cross-attention in FPAM, which enables the fidelity stream to anchor the structural layout while the perce… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of the distribution of various selected features on a per-pixel basis, including fidelity feature ˜ff (•), perceptual feature ˜fp (•), perception-to-fidelity feature ˜fpf (•), and fidelity-to-perception feature ˜ffp (•) [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
read the original abstract

Arbitrary-scale image super-resolution (ASISR) aims to reconstruct high-resolution images from low-resolution inputs over a continuous range of upscaling factors. While traditional pixel-regression approaches often produce overly smooth results that lack realistic details, recent diffusion methods can produce sharper and more realistic textures. However, these diffusion techniques frequently introduce the risk of structural hallucinations. To address these issues, we propose Fidelity- and Perception-Aware Local Implicit Attention (FPLIA), a framework that effectively integrates fidelity-oriented features into a diffusion pipeline to produce realistic and faithful reconstructions for ASISR. We introduce a Fidelity and Perception Attention Module (FPAM), which applies both self-attention and cross-attention to fidelity-oriented and perceptual features to enhance representational capacity. To further exploit their complements, we design a Fidelity and Perception Select Module (FPSM) that adaptively selects the most representative features for RGB values prediction. We conduct extensive experiments to validate the effectiveness of these components. Both qualitative and quantitative results show that FPLIA delivers superior perceptual realism while maintaining reconstruction accuracy on standard ASISR benchmarks. The source code is accessible at the following repository: https://github.com/XUSean0118/FPLIA.

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

0 major / 3 minor

Summary. The paper proposes Fidelity- and Perception-Aware Local Implicit Attention (FPLIA) for arbitrary-scale image super-resolution (ASISR). It integrates fidelity-oriented features into a diffusion pipeline via two new modules: the Fidelity and Perception Attention Module (FPAM), which combines self- and cross-attention on fidelity and perceptual features, and the Fidelity and Perception Select Module (FPSM), which adaptively selects features for RGB prediction. The authors claim that extensive experiments on standard ASISR benchmarks demonstrate superior perceptual realism while preserving reconstruction accuracy, and they release the source code publicly.

Significance. If the reported gains hold under scrutiny, the work offers a concrete mechanism for reducing structural hallucinations in diffusion-based ASISR without sacrificing fidelity, which could influence hybrid fidelity-perception designs in image restoration more broadly. The public code release strengthens reproducibility.

minor comments (3)
  1. The abstract asserts quantitative superiority on benchmarks but supplies no numerical values, ablation tables, or error metrics; adding at least one representative table or set of PSNR/LPIPS numbers in the abstract or §4 would make the central claim immediately verifiable.
  2. The description of how FPAM and FPSM are inserted into the diffusion pipeline (e.g., at which denoising step or feature level) remains high-level; a diagram or pseudocode in §3 would clarify integration without requiring readers to inspect the repository.
  3. Notation for the attention operations inside FPAM is not defined in the provided text; consistent variable names and a short equation block would improve readability.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, recognition of the work's potential impact on hybrid fidelity-perception designs, and recommendation for minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper proposes architectural modules (FPAM, FPSM) for integrating fidelity and perceptual features into a diffusion-based ASISR pipeline and reports empirical results on standard benchmarks with public code. No derivation chain, equations, or self-citations are load-bearing; claims rest on external validation rather than reducing to fitted parameters or self-referential definitions by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces two new modules (FPAM and FPSM) as engineering contributions on top of standard diffusion and attention mechanisms; no free parameters, axioms, or invented physical entities are described.

pith-pipeline@v0.9.1-grok · 5756 in / 998 out tokens · 21281 ms · 2026-06-26T12:24:01.561606+00:00 · methodology

discussion (0)

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Reference graph

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