WAV-DUC: a dynamic image enhancement algorithm for underwater environment

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorAlam, Md. Ashraful
dc.contributor.authorRahman, Washikur
dc.contributor.authorMahmud, Tausif
dc.contributor.authorArnob, Khondker Shahed
dc.contributor.authorChowdhury, Sadnima Amin
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2025-08-17T06:20:39Z
dc.date.available2025-08-17T06:20:39Z
dc.date.copyright2024
dc.date.issued2024-10
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 43-45).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.en_US
dc.description.abstractObject detection underwater is particularly difficult because of issues such as low illumination, illumination changes, and the scarcity of labeled underwater images. To overcome these issues, this paper introduces a new type of hybrid image enhancement method (Wav-DUC). The method incorporates the strengths of CLAHE, DCP, DWT, Unsharp Masking, and Bilateral filtering. While adopting the advantages of these techniques, the proposed method improves image quality under the various conditions of marine environments. The enhancement is done in such a way that the enhancement methods are selectively used above a predetermined threshold so as to enhance the image, resulting in an optimum result. testing the model with benchmark dataset [2], we achieved a PSNR score of 21.78 and an SSIM score of 0.8535. Also, the method has an average execution time of 64.78ms on Raspberry Pi 3. This proves the robustness and faster response time of the algorithm, making it suitable for edge devices.en_US
dc.description.degreeBachelor of Science in Computer Science and Engineering
dc.description.statementofresponsibilityRahman, Washikur
dc.description.statementofresponsibilityMahmud, Tausif
dc.description.statementofresponsibilityKhondker Shahed Arnob
dc.description.statementofresponsibilitySadnima Amin Chowdhury
dc.format.extent45 pages
dc.identifier.otherID 21301029
dc.identifier.otherID 21301345
dc.identifier.otherID 21301028
dc.identifier.otherID 21301260
dc.identifier.urihttp://hdl.handle.net/10361/26550
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectUnderwateren_US
dc.subjectRaspberry pien_US
dc.subjectUnsharp maskingen_US
dc.subjectTransfer learningen_US
dc.subjectBilateral filteringen_US
dc.subject.lcshMarine biology.
dc.subject.lcshMachine learning.
dc.subject.lcshArtificial intelligence.
dc.subject.lcshComputer vision.
dc.titleWAV-DUC: a dynamic image enhancement algorithm for underwater environmenten_US
dc.typeThesis

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