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REVIEW 4 major objections 4 minor 42 references

Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims that the bior6.8 biorthogonal wavelet with universal thresholding at decomposition levels 2–3 is the consistently best classical configuration for denoising MRI brain images under the tested noise conditions.

desk verdict Unreviewable as submitted: readable abstract, unreadable body, and a plausible but unvalidated optimum. read the letter →

arxiv 2508.15011 v1 pith:WBA3XV4Q submitted 2025-08-20 eess.IV

Asadullah Bin Rahman , Masud Ibn Afjal , Md. Abdulla Al Mamun This is my paper
classification eess.IV
keywords waveletdenoisingMRIbrainimagingbiorthogonaluniversalthresholddecompositionlevelimagequalityassessmentnoisereductionmedicalenhancement
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper sets out to settle a practical question: which classical wavelet-denoising configuration should be the default for MRI brain images? It systematically varies the wavelet family, the threshold rule, and the number of decomposition levels, applies the pipeline to brain images under several injected noise conditions, and ranks the results with quantitative quality scores. The paper's central claim is that the bior6.8 biorthogonal wavelet with universal thresholding at decomposition levels 2–3 is consistently best, suppressing noise while retaining anatomical boundaries and diagnostic features. If that claim holds, imaging teams can use this one recipe as a parameter-light preprocessing step without per-image tuning.

What carries the argument

The carrying mechanism is the discrete wavelet transform applied as a multilevel image decomposition, with three knobs that the paper turns: the wavelet family (the bior6.8 biorthogonal pair, whose symmetric filters avoid phase distortion), the threshold rule (universal thresholding, which sets one threshold from the estimated noise level and shrinks detail coefficients), and the decomposition level (how many pyramid layers of low/high-frequency subbands are separated). The search grid over these knobs, evaluated on denoised MRI brain slices under multiple noise conditions with quantitative quality metrics, is what produces the claimed optimum.

What would settle it

Take the same sweep and run it on images degraded with Rician noise (the noise model for magnitude MRI) or on a physical phantom scanned on an MRI system; if any other wavelet-threshold-depth combination ties or beats bior6.8 plus universal thresholding at levels 2–3, the claimed optimality is limited to the synthetic setting.

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Extended reading notes

Core claim

The authors report an empirical optimum in the wavelet-denoising configuration space for MRI brain images. After sweeping wavelet families, threshold choices, and decomposition depths under varied noise conditions, they find that the bior6.8 biorthogonal wavelet, used with universal thresholding and two to three decomposition levels, achieves the best balance between noise removal and preservation of anatomical detail. The winning configuration is offered as a general benchmark for MRI brain denoising and as a recommended default before downstream enhancement steps such as histogram equalization.

Load-bearing premise

The load-bearing premise is that the synthetic noise injected into the test images and the quality scores used to rank outputs represent real MRI noise and real diagnostic value, so the configuration that wins the benchmark will also preserve what a clinician needs to see.

Editorial extensions

If this is right

  • A fixed default recipe—bior6.8, universal thresholding, levels 2–3—can be used for MRI brain denoising without per-image parameter tuning.
  • Denoising with the winning configuration before contrast enhancement should prevent histogram equalization from amplifying noise and salt-and-pepper artifacts.
  • The reported benchmarks give future denoising methods a classical wavelet baseline to beat.
  • The winning configuration preserves anatomical structure and diagnostic features, not just smoothness, under the tested noise conditions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: because the paper only injects synthetic noise, the winning configuration may not transfer to real scanner noise such as Rician or spatially varying noise; the authors do not test that transfer.
  • Inference: the shallow optimal depth (levels 2–3) suggests diagnostically relevant structure lives in coarse-to-mid subbands and deeper decomposition only over-smooths; the paper does not examine this mechanism directly.
  • Inference: the same sweep on CT or ultrasound could produce a different winner, since those modalities have different noise statistics and structure scales; nothing in the paper claims the bior6.8 result generalizes across modalities.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper claims to systematically evaluate wavelet-based denoising on MRI brain images and to identify the bior6.8 biorthogonal wavelet with universal thresholding at decomposition levels 2–3 as consistently optimal across various noise conditions, providing significant noise reduction while preserving diagnostic features. The only readable scientific content is the abstract; the full text is a non-decodable encoded dump and includes a stray header from arXiv:2508.15007v1 (physics.chem-ph). No methods, dataset description, noise model, numerical tables, figures, or statistical results are available for audit.

Significance. If substantiated, a rigorous comparison of classical wavelet denoising configurations for MRI brain images would be a useful practical benchmark. However, the submitted manuscript provides no auditable evidence. It contains neither machine-checked derivations, reproducible code, nor readable empirical results. The central optimal configuration is asserted in the abstract but cannot be checked. The clinical-diagnostic claim is not supported by any reported clinical or observer-based outcome.

major comments (4)
  1. [Full Text] The full text is an unreadable encoded dump, and the header from arXiv:2508.15007v1 [physics.chem-ph] appears inside the body. This prevents verification of the methodology, equations, dataset, noise model, results, and supporting tables. The central claim rests entirely on the abstract, which is insufficient for a reviewable scientific manuscript.
  2. [Abstract] The abstract does not specify the noise model. Universal thresholding (Donoho–Johnstone) is derived for additive white Gaussian noise with known variance, whereas MRI magnitude images follow Rician/noncentral chi noise with signal-dependent variance. If the benchmark added synthetic Gaussian noise, the claim that bior6.8 with universal thresholding is 'consistently optimal' for MRI brain images may not transfer to realistic acquisition noise. The manuscript must state the noise model and report performance under Rician noise.
  3. [Abstract] The reported optimum appears to be selected by ranking candidate configurations on the same benchmark that is used to support the claim. No held-out validation, confidence intervals, multiple noise realizations, or cross-image statistics are reported. The word 'consistently' implies stability across images and noise levels, but no evidence for this stability is presented or readable.
  4. [Abstract] The abstract asserts that the optimal configuration preserves 'diagnostic features critical for clinical applications.' No diagnostic accuracy endpoints, reader studies, or clinical validation are described. Quantitative image-similarity metrics such as PSNR/SSIM do not necessarily reflect diagnostic value, so this clinical claim is unsupported and should be either substantiated or removed.
minor comments (4)
  1. [Abstract] The opening sentence mentions CT and ultrasound, but the study is specifically about MRI brain images; the scope introduction is inconsistent.
  2. [Abstract] Salt-and-pepper artifacts and histogram equalization are mentioned, but universal wavelet thresholding is not the standard tool for impulse noise. The relevance of these artifacts to the study is unclear.
  3. [Full Text] The stray arXiv identifier from another paper indicates a compilation or upload error; the correct manuscript file should be submitted.
  4. [Full Text] No data availability, code availability, or reproducibility statement is visible.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: the reported optimum is an empirical benchmark ranking, not a derivation from fitted inputs or self-citation.

full rationale

The available readable portion of the manuscript (the abstract) claims that a systematic evaluation across noise conditions identifies bior6.8 with universal thresholding at decomposition levels 2–3 as consistently optimal. This is a model-selection/benchmark claim: candidate configurations are scored on external quality metrics (e.g., PSNR/SSIM-type measures) and the best performer is reported. The conclusion is not a prediction derived from a fitted parameter, and it does not invoke a self-citation chain or a uniqueness theorem. Universal thresholding is a fixed, externally defined rule, not a parameter fitted to the test data. The fact that the winning configuration is selected by ranking on the same benchmark that supports the claim is normal empirical practice, not circularity: the metrics and noise conditions are independent of the method's construction. The unreadable/encoded full text prevents auditing specific equations, tables, or error bars, but that is a reproducibility/integrity concern, not a demonstrated circular step. Under the stated hard rule that circularity must be exhibited by quoting a specific reduction (Eq. X = Eq. Y by construction, or a fitted parameter renamed as prediction), no such reduction can be identified here. Concerns about transfer from Gaussian to Rician MRI noise and absence of clinical reader studies are validity concerns, not circularity, and are therefore not scored under this pass.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The review rests on the abstract because the supplied full text is not readable. The ledger lists the tuning choices that define the reported optimum, plus the domain assumptions needed to turn a benchmark score into a clinical recommendation.

free parameters (3)
  • Decomposition level = 2-3 (reported optimal)
    The optimality claim is the result of scanning decomposition depths; the selected depth is an output of the benchmark, not an independent prior.
  • Threshold rule = universal thresholding
    Universal thresholding is selected among alternatives and declared optimal on the same benchmark data used to report the finding.
  • Wavelet family = bior6.8
    The biorthogonal wavelet is chosen because it scored highest on the evaluation images, making the reported optimum contingent on the test set.
assumptions (3)
  • domain assumption Synthetic noise and quantitative image-quality metrics are valid proxies for real clinical MRI noise and for diagnostic value.
    The abstract claims diagnostic accuracy and preservation of anatomical features, but no phantom/scanner noise model or reader study is mentioned.
  • domain assumption The benchmark image set is representative of MRI brain imaging beyond the specific test images.
    No dataset or acquisition details are given in the abstract; generalizing the optimal configuration to other scanners assumes representativeness.
  • domain assumption Wavelet-domain thresholding does not remove or distort diagnostically relevant structures at the tested noise levels.
    The conclusion that bior6.8 at levels 2-3 preserves diagnostic features relies on this unstated assumption.

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Cite this review

Pith. "Pith review of Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks." pith.science (2026). https://pith.science/paper/WBA3XV4Q

@misc{pith2026250815011,
  author       = {Pith},
  title        = {Pith review of: Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WBA3XV4Q}},
  note         = {Machine review of arXiv:2508.15011}
}
read the original abstract

Medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound are essential for accurate diagnosis and treatment planning in modern healthcare. However, noise contamination during image acquisition and processing frequently degrades image quality, obscuring critical diagnostic details and compromising clinical decision-making. Additionally, enhancement techniques such as histogram equalization may inadvertently amplify existing noise artifacts, including salt-and-pepper distortions. This study investigates wavelet transform-based denoising methods for effective noise mitigation in medical images, with the primary objective of identifying optimal combinations of threshold values, decomposition levels, and wavelet types to achieve superior denoising performance and enhanced diagnostic accuracy. Through systematic evaluation across various noise conditions, the research demonstrates that the bior6.8 biorthogonal wavelet with universal thresholding at decomposition levels 2-3 consistently achieves optimal denoising performance, providing significant noise reduction while preserving essential anatomical structures and diagnostic features critical for clinical applications.

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Reference graph

Works this paper leans on

42 extracted references · 40 canonical work pages

  1. [1]

    A novel diffusivity function-based image denoising for MRI medical images

    Sreedhar Kollem, Katta Ramalinga Reddy, and Duggirala Srinivasa Rao. A novel diffusivity function-based image denoising for MRI medical images. Multimed Tools Appl , 82(21):32057--32089, September 2023

    work page 2023

  2. [2]

    A Review of Wavelet Analysis and Its Applications : Challenges and Opportunities

    Tiantian Guo, Tongpo Zhang, Enggee Lim, Miguel López-Benítez, Fei Ma, and Limin Yu. A Review of Wavelet Analysis and Its Applications : Challenges and Opportunities . IEEE Access , 10:58869--58903, 2022

    work page 2022

  3. [3]

    Efficient Denoising of Multi -modal Medical Image using Wavelet Transform and Singular Value Decomposition

    Rajesh Patil and Surendra Bhosale. Efficient Denoising of Multi -modal Medical Image using Wavelet Transform and Singular Value Decomposition . In 2023 IEEE IAS Global Conference on Emerging Technologies ( GlobConET ) , pages 1--6, London, United Kingdom, May 2023. IEEE

    work page 2023

  4. [4]

    A Study of Adaptive Fractional - Order Total Variational Medical Image Denoising

    Yanzhu Zhang, Tingting Liu, Fan Yang, and Qi Yang. A Study of Adaptive Fractional - Order Total Variational Medical Image Denoising . Fractal Fract , 6(9):508, September 2022

    work page 2022

  5. [5]

    El-Rabaie, Anas M

    Walid El-Shafai, Samy Abd El-Nabi, El-Sayed M. El-Rabaie, Anas M. Ali, Naglaa F. Soliman, Abeer D. Algarni, and Fathi E. Abd El-Samie. Efficient Deep - Learning - Based Autoencoder Denoising Approach for Medical Image Diagnosis . Computers, Materials & Continua , 70(3):6107--6125, 2022

    work page 2022

  6. [6]

    Image denoising in the deep learning era

    Saeed Izadi, Darren Sutton, and Ghassan Hamarneh. Image denoising in the deep learning era. Artif Intell Rev , 56(7):5929--5974, July 2023

    work page 2023

  7. [7]

    Multi-stage image denoising with the wavelet transform

    Chunwei Tian, Menghua Zheng, Wangmeng Zuo, Bob Zhang, Yanning Zhang, and David Zhang. Multi-stage image denoising with the wavelet transform. Pattern Recognition , 134:109050, February 2023

    work page 2023

  8. [8]

    Medical image denoising using convolutional denoising autoencoders

    Lovedeep Gondara. Medical image denoising using convolutional denoising autoencoders. In 2016 IEEE 16th International Conference on Data Mining Workshops ( ICDMW ) , pages 241--246, December 2016. arXiv:1608.04667 [cs]

    work page Pith review arXiv 2016

Show all 42 references
  1. [9]

    Brain Tumor Dataset , April 2017

    Jun Cheng . Brain Tumor Dataset , April 2017

    2017

  2. [10]

    Chipman, Hugh A

    Hugh A. Chipman, Hugh A. Chipman, Eric D. Kolaczyk, and Robert E. McCulloch. Adaptive bayesian wavelet shrinkage. Journal of the American Statistical Association , 92(440):1413--1421, December 1997

    1997

  3. [11]

    Ideal spatial adaptation by wavelet shrinkage

    David L Donoho and Iain M Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika , 81(3):425--455, September 1994

    1994

  4. [12]

    D. L. Donoho. De-noising by soft-thresholding. IEEE Trans. Inf. Theor. , 41(3):613--627, May 1995

    1995

  5. [13]

    Zhou Wang and Alan C. Bovik. Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures . IEEE Signal Processing Magazine , 26(1):98--117, January 2009

    2009

  6. [14]

    Bovik, H.R

    Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing , 13(4):600--612, April 2004

    2004

  7. [15]

    From Classical to Deep Learning : A Systematic Review of Image Denoising Techniques

    Hewa Majeed Zangana and Firas Mahmood Mustafa. From Classical to Deep Learning : A Systematic Review of Image Denoising Techniques . JICS , 3(1):50--65, July 2024

    2024

  8. [16]

    Denoising diffusion probabilistic models for 3D medical image generation

    Firas Khader, Gustav Müller-Franzes, Soroosh Tayebi Arasteh, Tianyu Han, Christoph Haarburger, Maximilian Schulze-Hagen, Philipp Schad, Sandy Engelhardt, Bettina Baeßler, Sebastian Foersch, Johannes Stegmaier, Christiane Kuhl, Sven Nebelung, Jakob Nikolas Kather, and Daniel Tr...

    2023

  9. [17]

    Alpana Sahu, K. P. S. Rana, and Vineet Kumar. An application of deep dual convolutional neural network for enhanced medical image denoising. Med Biol Eng Comput , 61(5):991--1004, May 2023

    2023

  10. [18]

    Seethalakshmi, P Hema, and Jitendra Chandrakant Musale

    Abhay Shukla, K. Seethalakshmi, P Hema, and Jitendra Chandrakant Musale. An Effective Approach for Image Denoising Using Wavelet Transform Involving Deep Learning Techniques . In 2023 4th International Conference on Smart Electronics and Communication ( ICOSEC ) , pages 1381--...

    2023

  11. [19]

    Optimal Deep CNN – Based Vectorial Variation Filter for Medical Image Denoising

    Dinesh Kumar Atal. Optimal Deep CNN – Based Vectorial Variation Filter for Medical Image Denoising . J Digit Imaging , 36(3):1216--1236, January 2023

    2023

  12. [20]

    A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis

    Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader, Soroosh Tayebi Arasteh, Christoph Haarburger, Christiane Kuhl, Tianci Wang, Tianyu Han, Teresa Nolte, Sven Nebelung, Jakob Nikolas Kather, and Daniel Truhn. A multimodal comparison of latent denoising diffusion probabili...

    2023

  13. [21]

    A Complete Review on Image Denoising Techniques for Medical Images

    Amandeep Kaur and Guanfang Dong. A Complete Review on Image Denoising Techniques for Medical Images . Neural Process Lett , 55(6):7807--7850, December 2023

    2023

  14. [22]

    StruNet : Perceptual and low‐rank regularized transformer for medical image denoising

    Yuhui Ma, Qifeng Yan, Yonghuai Liu, Jiang Liu, Jiong Zhang, and Yitian Zhao. StruNet : Perceptual and low‐rank regularized transformer for medical image denoising. Medical Physics , 50(12):7654--7669, December 2023

    2023

  15. [23]

    Shrivastava

    Divya Gautam, Kavita Khare, and Bhavana P. Shrivastava. A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising . Applied Sciences , 13(12):7032, June 2023

    2023

  16. [24]

    Medical Image Denoising Techniques : A Review

    Rajesh Patil and Surendra Bhosale. Medical Image Denoising Techniques : A Review . IJONEST , 4(1):21--33, January 2022

    2022

  17. [25]

    Malathy, J

    Sreedhar Kollem, Katta Ramalinga Reddy, Duggirala Srinivasa Rao, Chintha Rajendra Prasad, V. Malathy, J. Ajayan, and Deboraj Muchahary. Image denoising for magnetic resonance imaging medical images using improved generalized cross‐validation based on the diffusivity function. ...

    2022

  18. [26]

    Mohd Sagheer and Sudhish N

    Sameera V. Mohd Sagheer and Sudhish N. George. A review on medical image denoising algorithms. Biomedical Signal Processing and Control , 61:102036, August 2020

    2020

  19. [27]

    Medical image denoising using convolutional neural network: a residual learning approach

    Worku Jifara, Feng Jiang, Seungmin Rho, Maowei Cheng, and Shaohui Liu. Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput , 75(2):704--718, February 2019

    2019

  20. [28]

    Survey – Start with Image Denoising

    Benziane Sarâh. Survey – Start with Image Denoising . WSEAS TRANSACTIONS ON SIGNAL PROCESSING , 21:41--50, April 2025

    2025

  21. [29]

    Bhensdadia

    Ashishkumar Gor and C.K. Bhensdadia. Two self-supervised image denoiser designs with discrete wavelet transform and non-local means-based algorithms. 2576-8484 , 8(6), December 2024

    2024

  22. [30]

    Review of Hybrid Denoising Approaches in Face Recognition : Bridging Wavelet Transform and Deep Learning

    Hewa Majeed Zangana and Firas Mahmood Mustafa. Review of Hybrid Denoising Approaches in Face Recognition : Bridging Wavelet Transform and Deep Learning . ijcs , 13(4), July 2024

    2024

  23. [31]

    Mikhael, Jeremy P

    Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, and Alison Q. O’Neil. The role of noise in denoising models for anomaly detection in...

    2023

  24. [32]

    Hybrid Machine Learning Techniques for Image Denoising Based on Wavelet Transform

    Qikun Yuan. Hybrid Machine Learning Techniques for Image Denoising Based on Wavelet Transform . In 2024 IEEE 6th International Conference on Power , Intelligent Computing and Systems ( ICPICS ) , pages 1162--1169, Shenyang, China, July 2024. IEEE

    2024

  25. [33]

    Multi-level Wavelet - CNN for Image Restoration , 2018

    Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, and Wangmeng Zuo. Multi-level Wavelet - CNN for Image Restoration , 2018. Version Number: 2

    2018

  26. [34]

    Wavelet analysis model inspired convolutional neural networks for image denoising

    Ruotao Xu, Yong Xu, Xuhui Yang, Haoran Huang, Zhenghua Lei, and Yuhui Quan. Wavelet analysis model inspired convolutional neural networks for image denoising. Applied Mathematical Modelling , 125:798--811, January 2024

    2024

  27. [35]

    Hybrid Image Denoising Using Wavelet Transform and Deep Learning

    Hewa Majeed Zangana and Firas Mahmood Mustafa. Hybrid Image Denoising Using Wavelet Transform and Deep Learning . EAI Endorsed Trans AI Robotics , 3, November 2024

    2024

  28. [36]

    Densely Self -guided Wavelet Network for Image Denoising

    Wei Liu, Qiong Yan, and Yuzhi Zhao. Densely Self -guided Wavelet Network for Image Denoising . In 2020 IEEE / CVF Conference on Computer Vision and Pattern Recognition Workshops ( CVPRW ) , pages 1742--1750, Seattle, WA, USA, June 2020. IEEE

    2020

  29. [37]

    Deep learning on image denoising: An overview

    Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin. Deep learning on image denoising: An overview. Neural Networks , 131:251--275, November 2020

    2020

  30. [38]

    Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet

    Nasser Edinne Benhassine, Abdelnour Boukaache, and Djalil Boudjehem. Medical image denoising using optimal thresholding of wavelet coefficients with selection of the best decomposition level and mother wavelet. International Journal of Imaging Systems and Technology , 31(4):19...

    1906 doi

  31. [39]

    Noise2Noise : Learning Image Restoration without Clean Data , October 2018

    Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2Noise : Learning Image Restoration without Clean Data , October 2018. arXiv:1803.04189 [cs]

    2018 arXiv

  32. [40]

    Plug-and- Play Image Restoration with Deep Denoiser Prior , July 2021

    Kai Zhang, Yawei Li, Wangmeng Zuo, Lei Zhang, Luc Van Gool, and Radu Timofte. Plug-and- Play Image Restoration with Deep Denoiser Prior , July 2021. arXiv:2008.13751 [eess]

    2021 arXiv

  33. [41]

    SwinIR : Image Restoration Using Swin Transformer , August 2021

    Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. SwinIR : Image Restoration Using Swin Transformer , August 2021. arXiv:2108.10257 [eess]

    2021 arXiv

  34. [42]

    Abdulla Al Mamun

    Asadullah Bin Rahman, Masud Ibn Afjal, and Md. Abdulla Al Mamun. Mitigating Noise from Biomedical Images Using Wavelet Transform Techniques . In 2025 International Conference on Electrical , Computer and Communication Engineering ( ECCE ) , pages 1--6, February 2025

    2025

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Reviewed August 5, 2026 · model on record in the stance chip above.