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arxiv: 2606.28453 · v1 · pith:SOIN67GQnew · submitted 2026-06-26 · 📡 eess.IV · cs.CV

DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling

Xizhuo Zhang , Yannian Gu , Zhongzhen Huang , Shaoting Zhang , Xiaofan Zhang This is my paper

Pith reviewed 2026-06-30 01:20 UTC · model grok-4.3

classification 📡 eess.IV cs.CV
keywords low-dose CT denoisingvisual autoregressive modelinggenerative frameworkresidual refinerhybrid decodermedical image denoisingnext-scale prediction
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The pith

DeVAR uses visual autoregressive modeling to denoise low-dose CT images by generating normal-dose CT token maps from LDCT global context and refining residuals.

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

The paper proposes DeVAR, a framework that applies visual autoregressive modeling to the problem of denoising low-dose CT scans. It conditions the generation of normal-dose CT on prefix tokens from the low-dose input and uses next-scale prediction to build the image progressively. A residual refiner and hybrid decoder are added to recover details lost to token quantization. This matters because it offers a new generative way to maintain image quality at lower radiation doses, which could reduce patient risk in medical imaging.

Core claim

DeVAR is a generative framework that applies visual autoregressive modeling to LDCT denoising for the first time. Conditioned on global context provided by LDCT prefix tokens, it progressively generates discrete token maps of the target NDCT via next-scale prediction. A residual refiner captures subtle anatomical structures beyond the discrete codebook, and a dual-representation hybrid training strategy allows the hybrid NDCT decoder to integrate continuous and discrete latents for high-fidelity reconstruction, leading to superior performance on two public datasets.

What carries the argument

Visual autoregressive modeling with next-scale prediction conditioned on LDCT prefix tokens, augmented by a residual refiner and dual-representation hybrid NDCT decoder.

If this is right

  • Superior qualitative and quantitative results on public LDCT datasets compared to existing methods.
  • Improved preservation of fine anatomical details through global-to-local dependency capture.
  • Effective handling of quantization losses via the residual refiner.
  • The hybrid training enables seamless integration of discrete and continuous representations.

Where Pith is reading between the lines

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

  • Similar autoregressive conditioning could be tested on other denoising tasks like MRI or ultrasound.
  • If the approach scales, it might reduce the need for high radiation in routine scans.
  • The next-scale prediction mechanism may generalize to other image restoration problems where structure is hierarchical.

Load-bearing premise

That conditioning on global LDCT prefix tokens and next-scale autoregressive prediction will intrinsically capture global-to-local structural dependencies better than prior deep-learning approaches.

What would settle it

A head-to-head comparison on the two public datasets where DeVAR fails to exceed the best existing method in metrics such as PSNR, SSIM, or visual detail preservation.

Figures

Figures reproduced from arXiv: 2606.28453 by Shaoting Zhang, Xiaofan Zhang, Xizhuo Zhang, Yannian Gu, Zhongzhen Huang.

Figure 1
Figure 1. Figure 1: Overview of our proposed DeVAR, a novel LDCT denoising framework with VAR, containing: (a) Dual-Latent Hybrid Training strategy designed to train a hybrid NDCT decoder. (b) Autoregressive Transformer with Residual Refiner. We propose DeVAR ( [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative comparison of different methods on the Mayo-2020. NDCTs, we also use three non-reference metrics: MANIQA [26], CLIPIQA [27] and MUSIQ [28]. As demonstrated in [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
read the original abstract

Computed tomography (CT) plays a crucial role in medical diagnosis, but minimizing radiation exposure while maintaining image quality remains a critical challenge. Low-dose CT (LDCT) protocols reduce radiation risks but inevitably suffer from severe noise and artifacts that compromise diagnostic accuracy. While existing deep learning methods have achieved promising results, there remains a continuous quest for generative paradigms that intrinsically capture global-to-local structural dependencies to better preserve fine anatomical details. To this end, we propose DeVAR, a novel generative framework that applies visual autoregressive modeling (VAR) to LDCT denoising for the first time. Conditioned on global context provided by LDCT prefix tokens, DeVAR progressively generates discrete token maps of the target normal-dose CT (NDCT) via next-scale prediction. Because quantization inherently discards high-frequency information, we introduce a residual refiner to capture subtle anatomical structures beyond the capacity of a discrete codebook. Finally, empowered by a dual-representation hybrid training strategy, our hybrid NDCT decoder seamlessly integrates continuous and discrete latents to reconstruct high-fidelity, detail-preserved images. Extensive experiments on two public datasets demonstrate that DeVAR consistently achieves superior qualitative and quantitative performance compared to state-of-the-art LDCT denoising methods.

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 DeVAR, a novel generative framework applying visual autoregressive modeling (VAR) to low-dose CT (LDCT) denoising for the first time. It conditions on global LDCT prefix tokens to progressively generate discrete token maps of normal-dose CT (NDCT) via next-scale prediction, adds a residual refiner to recover high-frequency anatomical details lost to quantization, and employs a dual-representation hybrid decoder integrating continuous and discrete latents. The authors claim that extensive experiments on two public datasets demonstrate consistent superiority in both qualitative and quantitative performance over state-of-the-art LDCT denoising methods.

Significance. If the superiority claims hold under detailed scrutiny, this work would mark a meaningful contribution by extending visual autoregressive modeling to medical image denoising, offering a new way to model global-to-local structural dependencies while mitigating quantization losses through the residual refiner and hybrid decoder. The novelty of applying VAR in this domain and the hybrid training strategy could influence subsequent research on detail-preserving generative models for low-radiation imaging.

major comments (1)
  1. [Experiments] The central claim of consistent superiority over SOTA methods is load-bearing for the paper's contribution, yet the provided manuscript text (including the abstract) contains no quantitative metrics, dataset sizes, error bars, statistical tests, ablation results, or baseline implementation details. This prevents verification of the performance gains or ruling out post-hoc data choices, as noted in the review constraints.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for detailed experimental reporting. We agree that quantitative metrics, dataset details, error bars, statistical tests, ablations, and baseline information are essential to substantiate the superiority claims and enable verification. We will revise the manuscript to include a comprehensive Experiments section addressing all points raised.

read point-by-point responses

  1. Referee: [Experiments] The central claim of consistent superiority over SOTA methods is load-bearing for the paper's contribution, yet the provided manuscript text (including the abstract) contains no quantitative metrics, dataset sizes, error bars, statistical tests, ablation results, or baseline implementation details. This prevents verification of the performance gains or ruling out post-hoc data choices, as noted in the review constraints.

    Authors: We agree with this assessment. The current manuscript draft does not include the requested quantitative details in the provided text. In the revised version, we will expand the Experiments section to report: (1) quantitative metrics (PSNR, SSIM, RMSE) with means and standard deviations (error bars) computed over the test sets; (2) dataset sizes and splits for the two public datasets; (3) statistical significance tests (e.g., paired t-tests or Wilcoxon tests) comparing DeVAR to baselines; (4) full ablation studies on the residual refiner, hybrid decoder, and next-scale prediction components; and (5) implementation details for all baselines, including training protocols and hyperparameters used for reproduction. These additions will directly support the claims and allow independent verification. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The provided abstract and method description introduce DeVAR as a novel application of visual autoregressive modeling (VAR) to LDCT denoising, with components like next-scale prediction, residual refiner, and hybrid decoder described at a high level without any equations, fitted parameters renamed as predictions, or self-citations that bear the central claim. No derivation chain reduces outputs to inputs by construction, and performance superiority is asserted via external experimental comparisons on public datasets rather than internal self-referential logic. This is a standard case of a self-contained proposal whose validity rests on empirical benchmarks outside the paper's own definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the method description relies on standard concepts of autoregressive token prediction and quantization without further elaboration.

pith-pipeline@v0.9.1-grok · 5757 in / 1094 out tokens · 39550 ms · 2026-06-30T01:20:27.326045+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

29 extracted references · 3 canonical work pages · 2 internal anchors

  1. [1]

    A non-local algorithm for image denoising[C]//2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05)

    Buades A, Coll B, Morel J M. A non-local algorithm for image denoising[C]//2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05). Ieee, 2005, 2: 60-65

    2005

  2. [2]

    Image denoising by sparse 3-D transform- domain collaborative filtering[J]

    Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform- domain collaborative filtering[J]. IEEE Transactions on image processing, 2007, 16(8): 2080-2095

    2007

  3. [3]

    Low-dose computed tomography image restoration using previous normal-dose scan[J]

    Ma J, Huang J, Feng Q, et al. Low-dose computed tomography image restoration using previous normal-dose scan[J]. Medical physics, 2011, 38(10): 5713-5731

    2011

  4. [4]

    Denoised and texture enhanced MVCT to improve soft tissue conspicuity[J]

    Sheng K, Gou S, Wu J, et al. Denoised and texture enhanced MVCT to improve soft tissue conspicuity[J]. Medical physics, 2014, 41(10): 101916

    2014

  5. [5]

    Low-dose CT with a residual encoder-decoder convolutional neural network[J]

    Chen H, Zhang Y, Kalra M K, et al. Low-dose CT with a residual encoder-decoder convolutional neural network[J]. IEEE transactions on medical imaging, 2017, 36(12): 2524-2535

    2017

  6. [6]

    Edcnn: Edge enhancement-based densely connected net- work with compound loss for low-dose ct denoising[C]//2020 15th IEEE International conference on signal processing (ICSP)

    Liang T, Jin Y, Li Y, et al. Edcnn: Edge enhancement-based densely connected net- work with compound loss for low-dose ct denoising[C]//2020 15th IEEE International conference on signal processing (ICSP). IEEE, 2020, 1: 193-198

    2020

  7. [7]

    Low-dose CT image denoising using a generative ad- versarial network with Wasserstein distance and perceptual loss[J]

    Yang Q, Yan P, Zhang Y, et al. Low-dose CT image denoising using a generative ad- versarial network with Wasserstein distance and perceptual loss[J]. IEEE transactions on medical imaging, 2018, 37(6): 1348-1357

    2018

  8. [8]

    DU-GAN: Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising[J]

    Huang Z, Zhang J, Zhang Y, et al. DU-GAN: Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 71: 1-12

    2021

  9. [9]

    ASCON: Anatomy-aware supervised contrastive learning framework for low-dose CT denoising[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention

    Chen Z, Gao Q, Zhang Y, et al. ASCON: Anatomy-aware supervised contrastive learning framework for low-dose CT denoising[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023: 355-365

    2023

  10. [10]

    CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization[J]

    Gao Q, Li Z, Zhang J, et al. CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization[J]. IEEE Transactions on Medical Imaging, 2023, 43(2): 745-759

    2023

  11. [11]

    Taming transformers for high-resolution image synthesis[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Esser P, Rombach R, Ommer B. Taming transformers for high-resolution image synthesis[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 12873-12883

    2021

  12. [12]

    Attention is all you need[J]

    Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30

    2017

  13. [13]

    Visual autoregressive modeling: Scalable image generation via next-scale prediction[J]

    Tian K, Jiang Y, Yuan Z, et al. Visual autoregressive modeling: Scalable image generation via next-scale prediction[J]. Advances in neural information processing systems, 2024, 37: 84839-84865

    2024

  14. [14]

    Scalable diffusion models with transformers[C]//Proceedings of the IEEE/CVF international conference on computer vision

    Peebles W, Xie S. Scalable diffusion models with transformers[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2023: 4195-4205

    2023

  15. [15]

    Hart: Efficient visual generation with hybrid autoregressive transformer,

    Tang H, Wu Y, Yang S, et al. Hart: Efficient visual generation with hybrid autore- gressive transformer[J]. arXiv preprint arXiv:2410.10812, 2024

    work page arXiv 2024

  16. [16]

    Classifier-Free Diffusion Guidance

    Ho J, Salimans T. Classifier-free diffusion guidance[J]. arXiv preprint arXiv:2207.12598, 2022. 10 X. Zhang et al

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  17. [17]

    Low-dose CT for the detection and classification of metastatic liver lesions: results of the 2016 low dose CT grand challenge[J]

    McCollough C H, Bartley A C, Carter R E, et al. Low-dose CT for the detection and classification of metastatic liver lesions: results of the 2016 low dose CT grand challenge[J]. Medical physics, 2017, 44(10): e339-e352

    2016

  18. [18]

    Low-dose CT image and projection dataset[J]

    Moen T R, Chen B, Holmes III D R, et al. Low-dose CT image and projection dataset[J]. Medical physics, 2021, 48(2): 902-911

    2021

  19. [19]

    Language models are unsupervised multitask learners[J]

    Radford A, Wu J, Child R, et al. Language models are unsupervised multitask learners[J]. OpenAI blog, 2019, 1(8): 9

    2019

  20. [20]

    Decoupled Weight Decay Regularization

    Loshchilov I, Hutter F. Fixing weight decay regularization in adam[J]. arXiv preprint arXiv:1711.05101, 2017, 5(5): 5

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  21. [21]

    Deep residual learning for image recogni- tion[C]//Proceedings of the IEEE conference on computer vision and pattern recog- nition

    He K, Zhang X, Ren S, et al. Deep residual learning for image recogni- tion[C]//Proceedings of the IEEE conference on computer vision and pattern recog- nition. 2016: 770-778

    2016

  22. [22]

    Autoregressive image generation without vector quantiza- tion[J]

    Li T, Tian Y, Li H, et al. Autoregressive image generation without vector quantiza- tion[J]. Advances in Neural Information Processing Systems, 2024, 37: 56424-56445

    2024

  23. [23]

    Neural discrete representation learning[J]

    Van Den Oord A, Vinyals O. Neural discrete representation learning[J]. Advances in neural information processing systems, 2017, 30

    2017

  24. [24]

    CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising[J]

    Wang D, Fan F, Wu Z, et al. CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising[J]. Physics in Medicine & Biology, 2023, 68(6): 065012

    2023

  25. [25]

    Hformer: highly efficient vision transformer for low-dose CT denoising[J]

    Zhang S Y, Wang Z X, Yang H B, et al. Hformer: highly efficient vision transformer for low-dose CT denoising[J]. Nuclear Science and Techniques, 2023, 34(4): 61

    2023

  26. [26]

    Maniqa: Multi-dimension attention network for no- reference image quality assessment[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Yang S, Wu T, Shi S, et al. Maniqa: Multi-dimension attention network for no- reference image quality assessment[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 1191-1200

    2022

  27. [27]

    Exploring clip for assessing the look and feel of images[C]//Proceedings of the AAAI conference on artificial intelligence

    Wang J, Chan K C K, Loy C C. Exploring clip for assessing the look and feel of images[C]//Proceedings of the AAAI conference on artificial intelligence. 2023, 37(2): 2555-2563

    2023

  28. [28]

    Musiq: Multi-scale image quality trans- former[C]//Proceedings of the IEEE/CVF international conference on computer vision

    Ke J, Wang Q, Wang Y, et al. Musiq: Multi-scale image quality trans- former[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 5148-5157

    2021

  29. [29]

    Image quality assessment: from error visibility to structural similarity[J]

    Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE transactions on image processing, 2004, 13(4): 600-612

    2004