A Systematic Review of Wavelet-Based Pooling for Enhancing GANs: Central Trends, Auxiliary Pooling Strategies, and Complementary Wavelet Integrations

A Systematic Review of Wavelet-Based Pooling for Enhancing GANs: Central Trends, Auxiliary Pooling Strategies, and Complementary Wavelet Integrations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review A Systematic Review of Wavelet-Based Pooling for Enhancing GANs: Central Trends, Auxiliary Pooling Strategies, and Complementary Wavelet Integrations Shimaa Elbana, Ahmad Al-Kabbany, Said El-Khamy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8321844/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Generative Adversarial Networks (GANs) have revolutionized data synthesis across various domains, including medical image analysis, computer vision, and signal processing. Despite their success, GANs often face challenges such as training instability, mode collapse, and suboptimal image quality. Recent advancements have introduced wavelet transforms and specialized pooling techniques as architectural enhancements to address these issues. This systematic review primarily evaluates the impact of integrating wavelet-based pooling mechanisms into GAN architectures, as a novel approach to improving generative performance. In support of this central focus, we also examine other emerging pooling strategies as well as broader uses of wavelet transforms within GAN pipelines (e.g., in feature extraction or loss design). It compares these approaches against traditional GAN models and other generative frameworks to assess improvements in performance, stability, and computational efficiency. Following a comprehensive literature search across major databases, studies were selected based on predefined inclusion and exclusion criteria aligned with the PICO framework. The review focuses on GAN variants (e.g., DCGAN, StyleGAN, CycleGAN) incorporating wavelet-based decompositions, various pooling techniques (average, max, adaptive, and mixed), and hybrid approaches. Outcome measures include image quality metrics (FID, IS, PSNR, SSIM), training stability, feature preservation, and application-specific metrics such as diagnostic accuracy in medical image analysis. The findings indicate that wavelet and advanced pooling techniques contribute significantly to performance enhancements in GANs. Notable improvements include sharper image generation, better texture preservation, reduced mode collapse, and increased robustness to adversarial perturbations. In some cases, computational efficiency was also improved through multi-resolution representations. By foregrounding wavelet-based pooling and situating related techniques in context, this review provides a structured synthesis of architectural trends that enhance GAN performance across domains. These techniques provide architectural robustness and adaptability across diverse applications, warranting further exploration and standardization in future research. Artificial Intelligence and Machine Learning Biomedical Engineering Electrical Engineering GAN Wavelet Transform Pooling Systematic Review Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8321844","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":557917149,"identity":"43b4b03c-7642-4007-b2dc-4da4f775c0dd","order_by":0,"name":"Shimaa Elbana","email":"data:image/png;base64,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","orcid":"","institution":"Alexandria Higher Institute of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Shimaa","middleName":"","lastName":"Elbana","suffix":""},{"id":557917150,"identity":"688c2c78-92cc-4f79-90bc-4e63cfc8718e","order_by":1,"name":"Ahmad Al-Kabbany","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACdjYGZsYGEKOxgUgtzDAtPAdJ1iKRQKQO/ma2xM+FO+zyDG4+bnzwoYJBzrx/AX4tEofZDkvPPJNcbHA7sdlwxhkGY5kbDwhYc5i9QZq3jTlxw+3ENiCDIXGGxAH8OuQPszf/5m2rT9xw82D777//iNBicJjtGNDww4kbbjC2gcIhcQZ/A34thofZ0qxnnjmeOPNMYrNkzzEJYwkJAl6RO95mfLtwR3Vi3/HjDz/8qLGRk+An4DA4UIAolCAhguQbYCyibRkFo2AUjIKRAgB5iki4qo9YOAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3941-0061","institution":"Arab Academy for Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Al-Kabbany","suffix":""},{"id":557917151,"identity":"6b344c4f-8c18-483f-87f4-40f24b8585f6","order_by":2,"name":"Said El-Khamy","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"","lastName":"El-Khamy","suffix":""}],"badges":[],"createdAt":"2025-12-10 01:22:35","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8321844/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8321844/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98424604,"identity":"0d4ec2c1-9513-45c1-b8cc-5cdc3ccba0ab","added_by":"auto","created_at":"2025-12-17 16:33:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6274131,"visible":true,"origin":"","legend":"","description":"","filename":"AReviewonWaveletbasedPoolingforGANs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8321844/v1_covered_8009cc0d-548a-40e7-bb54-b06902254455.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Systematic Review of Wavelet-Based Pooling for Enhancing GANs: Central Trends, Auxiliary Pooling Strategies, and Complementary Wavelet Integrations\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Arab Academy for Science, Technology and Maritime Transport","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GAN, Wavelet Transform, Pooling, Systematic Review","lastPublishedDoi":"10.21203/rs.3.rs-8321844/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8321844/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenerative Adversarial Networks (GANs) have revolutionized data synthesis across various domains, including medical image analysis, computer vision, and signal processing. Despite their success, GANs often face challenges such as training instability, mode collapse, and suboptimal image quality. Recent advancements have introduced wavelet transforms and specialized pooling techniques as architectural enhancements to address these issues.\u003c/p\u003e\n\u003cp\u003eThis systematic review primarily evaluates the impact of integrating wavelet-based pooling mechanisms into GAN architectures, as a novel approach to improving generative performance. In support of this central focus, we also examine other emerging pooling strategies as well as broader uses of wavelet transforms within GAN pipelines (e.g., in feature extraction or loss design). It compares these approaches against traditional GAN models and other generative frameworks to assess improvements in performance, stability, and computational efficiency. Following a comprehensive literature search across major databases, studies were selected based on predefined inclusion and exclusion criteria aligned with the PICO framework. The review focuses on GAN variants (e.g., DCGAN, StyleGAN, CycleGAN) incorporating wavelet-based decompositions, various pooling techniques (average, max, adaptive, and mixed), and hybrid approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOutcome measures include image quality metrics (FID, IS, PSNR, SSIM), training stability, feature preservation, and application-specific metrics such as diagnostic accuracy in medical image analysis. The findings indicate that wavelet and advanced pooling techniques contribute significantly to performance enhancements in GANs. Notable improvements include sharper image generation, better texture preservation, reduced mode collapse, and increased robustness to adversarial perturbations. In some cases, computational efficiency was also improved through multi-resolution representations. By foregrounding wavelet-based pooling and situating related techniques in context, this review provides a structured synthesis of architectural trends that enhance GAN performance across domains. These techniques provide architectural robustness and adaptability across diverse applications, warranting further exploration and standardization in future research.\u003c/p\u003e","manuscriptTitle":"A Systematic Review of Wavelet-Based Pooling for Enhancing GANs: Central Trends, Auxiliary Pooling Strategies, and Complementary Wavelet Integrations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 14:01:31","doi":"10.21203/rs.3.rs-8321844/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be6ccb3e-7ced-4093-8154-a4c6ad3ffa2f","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59383792,"name":"Artificial Intelligence and Machine Learning"},{"id":59383793,"name":"Biomedical Engineering"},{"id":59383794,"name":"Electrical Engineering"}],"tags":[],"updatedAt":"2025-12-11T14:01:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 14:01:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8321844","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8321844","identity":"rs-8321844","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}