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arxiv: 2602.11553 · v2 · pith:UH7MFSB5new · submitted 2026-02-12 · 💻 cs.CV · cs.AI

Perception-based Image Denoising via Generative Compression

Nam Nguyen , Thinh Nguyen , Bella Bose This is my paper

Pith reviewed 2026-05-21 13:08 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords image denoisinggenerative compressionperceptual qualityWasserstein GANdiffusion modelsrate-distortion-perceptionnon-asymptotic guarantees
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The pith

Generative compression reconstructs noisy images from entropy-coded latents using perceptual losses to achieve better realism than distortion-driven methods.

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

This paper develops a framework for image denoising that prioritizes perceptual quality over pure pixel accuracy. It achieves this by first compressing the noisy image into entropy-coded latent representations that capture low-complexity structure, then using generative models to decode realistic textures guided by measures like LPIPS and Wasserstein distance. Two versions are presented: one based on conditional Wasserstein GANs that explicitly trades off rate, distortion, and perception, and another using diffusion models for iterative refinement from the compressed latents. The work also derives theoretical guarantees on reconstruction error and decoding success for the maximum-likelihood version under Gaussian noise. If successful, this could produce denoised images that maintain natural details and textures even under strong noise or when the noise distribution shifts.

Core claim

The paper establishes that perception-based image denoising can be realized via a generative compression approach, where the input is encoded into entropy-coded latent representations enforcing low-complexity structure and then reconstructed using generative decoders optimized with perceptual losses such as LPIPS and Wasserstein distance. Complementary methods include a conditional WGAN that controls the rate-distortion-perception trade-off and a conditional diffusion strategy for guided iterative denoising. Non-asymptotic bounds are provided for the compression-based maximum-likelihood denoiser under additive Gaussian noise, covering reconstruction error and decoding error probability.

What carries the argument

The generative compression framework that uses entropy-coded latent representations to enforce structure and generative decoders with perceptual measures to recover textures.

If this is right

  • Perceptual quality improves consistently on synthetic and real-noise image benchmarks compared to distortion-only methods.
  • Competitive performance is maintained in terms of traditional distortion metrics.
  • Explicit control over the rate-distortion-perception trade-off is possible with the WGAN instantiation.
  • Non-asymptotic error bounds apply to the maximum-likelihood denoiser for Gaussian noise cases.

Where Pith is reading between the lines

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

  • The same latent-compression strategy could extend to related tasks such as deblurring or inpainting where perceptual realism matters.
  • Practical deployment would require checking whether the generative decoding step adds unacceptable latency in real-time settings.

Load-bearing premise

That entropy-coded latent representations enforce low-complexity structure while generative decoders reliably recover realistic textures via LPIPS and Wasserstein losses without introducing artifacts or distribution shifts.

What would settle it

If perceptual metrics such as LPIPS show no improvement or if visible artifacts increase on real-noise benchmarks compared to standard denoisers, the central performance claim would be refuted.

read the original abstract

Image denoising aims to remove noise while preserving structural details and perceptual realism, yet distortion-driven methods often produce over-smoothed reconstructions, especially under strong noise and distribution shift. This paper proposes a generative compression framework for perception-based denoising, where restoration is achieved by reconstructing from entropy-coded latent representations that enforce low-complexity structure, while generative decoders recover realistic textures via perceptual measures such as learned perceptual image patch similarity (LPIPS) loss and Wasserstein distance. Two complementary instantiations are introduced: (i) a conditional Wasserstein GAN (WGAN)-based compression denoiser that explicitly controls the rate-distortion-perception (RDP) trade-off, and (ii) a conditional diffusion-based reconstruction strategy that performs iterative denoising guided by compressed latents. We further establish non-asymptotic guarantees for the compression-based maximum-likelihood denoiser under additive Gaussian noise, including bounds on reconstruction error and decoding error probability. Experiments on synthetic and real-noise benchmarks demonstrate consistent perceptual improvements while maintaining competitive distortion performance.

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

2 major / 2 minor

Summary. The manuscript proposes a generative compression framework for image denoising. Noisy images are mapped to entropy-coded latent representations that enforce low-complexity structure; generative decoders then reconstruct realistic textures using perceptual losses such as LPIPS and Wasserstein distance. Two instantiations are described: a conditional WGAN that explicitly trades off rate, distortion and perception, and a conditional diffusion model that performs iterative reconstruction guided by the compressed latents. Non-asymptotic guarantees are claimed for a compression-based maximum-likelihood denoiser under additive Gaussian noise, including bounds on reconstruction error and decoding error probability. Experiments on synthetic and real-noise benchmarks are reported to show perceptual gains while retaining competitive distortion performance.

Significance. If the non-asymptotic bounds can be shown to apply to the actual WGAN and diffusion objectives, and if the experiments supply clear, reproducible quantitative evidence of perceptual improvement, the work would provide a principled route to perception-aware denoising that avoids the over-smoothing typical of pure distortion minimization. The explicit RDP control in the WGAN instantiation and the use of compressed latents to guide diffusion are potentially useful technical contributions.

major comments (2)
  1. [Abstract] Abstract: The non-asymptotic guarantees on reconstruction error and decoding error probability are stated for the compression-based maximum-likelihood denoiser under additive Gaussian noise. The two concrete methods, however, optimize a conditional WGAN objective that includes LPIPS and Wasserstein losses and an iterative diffusion reconstruction; these objectives deviate from pure maximum-likelihood estimation and may violate the rate-distortion assumptions required for the stated bounds to hold.
  2. [Theoretical section] Theoretical section (where the guarantees are derived): No derivation steps, assumptions, or proof outline for the non-asymptotic bounds are supplied in the abstract or summary. Without these details it is impossible to assess whether the bounds remain valid once the perceptual terms used in the experiments are introduced.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by naming at least one dataset and reporting a concrete perceptual metric (e.g., LPIPS delta) rather than the generic statement of 'consistent perceptual improvements'.
  2. [Experiments] Ensure that all experimental baselines, exact training losses, and hyper-parameter choices for the WGAN and diffusion models are fully specified so that the claimed RDP trade-off can be reproduced.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The non-asymptotic guarantees on reconstruction error and decoding error probability are stated for the compression-based maximum-likelihood denoiser under additive Gaussian noise. The two concrete methods, however, optimize a conditional WGAN objective that includes LPIPS and Wasserstein losses and an iterative diffusion reconstruction; these objectives deviate from pure maximum-likelihood estimation and may violate the rate-distortion assumptions required for the stated bounds to hold.

    Authors: We agree that the non-asymptotic bounds are derived specifically for the compression-based maximum-likelihood denoiser under additive Gaussian noise. The WGAN and diffusion instantiations optimize perceptual objectives (LPIPS and Wasserstein) that go beyond pure MLE and may not satisfy the same assumptions. We do not claim the bounds apply directly to these perceptual methods. We will revise the abstract and add clarifying text in the introduction to explicitly distinguish the theoretical MLE results from the practical generative implementations. revision: yes

  2. Referee: [Theoretical section] Theoretical section (where the guarantees are derived): No derivation steps, assumptions, or proof outline for the non-asymptotic bounds are supplied in the abstract or summary. Without these details it is impossible to assess whether the bounds remain valid once the perceptual terms used in the experiments are introduced.

    Authors: We acknowledge that the current theoretical section states the bounds without providing derivation steps, assumptions, or a proof outline. This limits assessment of their scope. In the revision we will add a detailed proof sketch, explicit assumptions, and a clear statement that the bounds apply only to the MLE denoiser (separate from the perceptual losses in the WGAN and diffusion experiments). revision: yes

Circularity Check

0 steps flagged

No significant circularity; theoretical guarantees stated for idealized ML denoiser without reducing to fitted inputs or self-citations

full rationale

The paper's central claim establishes non-asymptotic bounds on reconstruction error and decoding error probability specifically for a compression-based maximum-likelihood denoiser under additive Gaussian noise. This derivation is presented as an independent theoretical result in the manuscript and does not reduce by construction to the LPIPS/Wasserstein losses or diffusion objectives used in the WGAN and iterative reconstruction instantiations. No self-definitional steps, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided abstract and context. The framework combines existing compression and generative elements in a new way rather than deriving results tautologically from prior self-references. The gap between the ML guarantees and the actual perceptual training losses is a potential correctness issue but does not constitute circularity per the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides insufficient detail to enumerate specific free parameters, axioms, or invented entities; the framework implicitly assumes standard properties of entropy coding and perceptual losses from prior work.

pith-pipeline@v0.9.0 · 5696 in / 1120 out tokens · 46680 ms · 2026-05-21T13:08:05.169326+00:00 · methodology

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Reference graph

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