aspg-3-4053-1791421115 20261008005835 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2026 21 2 Shokhan M. Al-Barzinji Department of Computer Networks Systems, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq Ahmed Abdullah Mahmood Al-Imam Al-Adham University College, Iraq Omar Muthanna Khudhur Department of Computer Engineering Techniques, College of Technical Engineering, University of Al Maarif, Al Anbar, 31001, Iraq Zaid Sami Mohsen Department of Computer Science and Information Technology, College of Science, University of Hilla, 51001 Babil, Iraq 16.00 Normal false false false EN-US X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> UnhideWhenUsed="true" Name="toc 1"/> UnhideWhenUsed="true" Name="toc 2"/> UnhideWhenUsed="true" Name="toc 3"/> UnhideWhenUsed="true" Name="toc 4"/> UnhideWhenUsed="true" Name="toc 5"/> UnhideWhenUsed="true" Name="toc 6"/> UnhideWhenUsed="true" Name="toc 7"/> UnhideWhenUsed="true" Name="toc 8"/> UnhideWhenUsed="true" Name="toc 9"/> Name="Normal Indent"/> Name="footnote text"/> Name="annotation text"/> Name="header"/> Name="footer"/> Name="index heading"/> UnhideWhenUsed="true" QFormat="true" Name="caption"/> Name="table of figures"/> Name="envelope address"/> Name="envelope return"/> Name="footnote reference"/> Name="annotation reference"/> Name="line number"/> Name="page number"/> Name="endnote reference"/> Name="endnote text"/> Name="table of authorities"/> Name="macro"/> Name="toa heading"/> Name="List"/> Name="List Bullet"/> Name="List Number"/> Name="List 2"/> Name="List 3"/> Name="List 4"/> Name="List 5"/> Name="List Bullet 2"/> Name="List Bullet 3"/> Name="List Bullet 4"/> Name="List Bullet 5"/> Name="List Number 2"/> Name="List Number 3"/> Name="List Number 4"/> Name="List Number 5"/> Name="Closing"/> Name="Signature"/> UnhideWhenUsed="true" Name="Default Paragraph Font"/> Name="Body Text"/> Name="Body Text Indent"/> Name="List Continue"/> Name="List Continue 2"/> Name="List Continue 3"/> Name="List Continue 4"/> Name="List Continue 5"/> Name="Message Header"/> Name="Salutation"/> Name="Date"/> Name="Body Text First Indent"/> Name="Body Text First Indent 2"/> Name="Note Heading"/> Name="Body Text 2"/> Name="Body Text 3"/> Name="Body Text Indent 2"/> Name="Body Text Indent 3"/> Name="Block Text"/> Name="Hyperlink"/> Name="FollowedHyperlink"/> Name="Document Map"/> Name="Plain Text"/> Name="E-mail Signature"/> Name="HTML Top of Form"/> Name="HTML Bottom of Form"/> Name="Normal (Web)"/> Name="HTML Acronym"/> Name="HTML Address"/> Name="HTML Cite"/> Name="HTML Code"/> Name="HTML Definition"/> Name="HTML Keyboard"/> Name="HTML Preformatted"/> Name="HTML Sample"/> Name="HTML Typewriter"/> Name="HTML Variable"/> Name="Normal Table"/> Name="annotation subject"/> Name="No List"/> Name="Outline List 1"/> Name="Outline List 2"/> Name="Outline List 3"/> Name="Table Simple 1"/> Name="Table Simple 2"/> Name="Table Simple 3"/> Name="Table Classic 1"/> Name="Table Classic 2"/> Name="Table Classic 3"/> Name="Table Classic 4"/> Name="Table Colorful 1"/> Name="Table Colorful 2"/> Name="Table Colorful 3"/> Name="Table Columns 1"/> Name="Table Columns 2"/> Name="Table Columns 3"/> Name="Table Columns 4"/> Name="Table Columns 5"/> Name="Table Grid 1"/> Name="Table Grid 2"/> Name="Table Grid 3"/> Name="Table Grid 4"/> Name="Table Grid 5"/> Name="Table Grid 6"/> Name="Table Grid 7"/> Name="Table Grid 8"/> Name="Table List 1"/> Name="Table List 2"/> Name="Table List 3"/> Name="Table List 4"/> Name="Table List 5"/> Name="Table List 6"/> Name="Table List 7"/> Name="Table List 8"/> Name="Table 3D effects 1"/> Name="Table 3D effects 2"/> Name="Table 3D effects 3"/> Name="Table Contemporary"/> Name="Table Elegant"/> Name="Table Professional"/> Name="Table Subtle 1"/> Name="Table Subtle 2"/> Name="Table Web 1"/> Name="Table Web 2"/> Name="Table Web 3"/> Name="Balloon Text"/> Name="Table Theme"/> Name="List Paragraph"/> Name="Intense Quote"/> Name="Subtle Emphasis"/> Name="Intense Emphasis"/> Name="Subtle Reference"/> Name="Intense Reference"/> UnhideWhenUsed="true" Name="Bibliography"/> UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/> Name="Grid Table 1 Light Accent 1"/> Name="Grid Table 6 Colorful Accent 1"/> Name="Grid Table 7 Colorful Accent 1"/> Name="Grid Table 1 Light Accent 2"/> Name="Grid Table 6 Colorful Accent 2"/> Name="Grid Table 7 Colorful Accent 2"/> Name="Grid Table 1 Light Accent 3"/> Name="Grid Table 6 Colorful Accent 3"/> Name="Grid Table 7 Colorful Accent 3"/> Name="Grid Table 1 Light Accent 4"/> Name="Grid Table 6 Colorful Accent 4"/> Name="Grid Table 7 Colorful Accent 4"/> Name="Grid Table 1 Light Accent 5"/> Name="Grid Table 6 Colorful Accent 5"/> Name="Grid Table 7 Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ansi-language:EN;} Vitreoretinal surgery is highly dependent on good visualization of fragile retinal surfaces for the purpose of accurate and safe operation. However, the image quality of current 3D heads-up display systems is often suboptimal, such as low contrast or inadequate sharpness, which is likely to decrease the accuracy of operation and prolong the operation duration. Improving intraoperative image quality continues to be a challenge for the advancement of the surgical results. In this paper, we advocate a deep learning-based solution to optimal imaging parameter guidance for the prospect of 3D HU-image guided VR surgery, seeking to improve vitreoretinal surface visibility during the surgery. A hybrid model that combines a U-Net-based image enhancement with a ViT for feature refinement has been learned using 212 manually optimized still frames (extracted from the ERM surgical video). The performance of the algorithm was quantitatively assessed through peak signal-to-noise ratio (PSNR) and the structural similarity index map (SSIM) and qualitatively evaluated in terms of the improvement in sharpness, brightness, and contrast. Moreover, the in-cabin usability of optimized images was investigated in an intraoperative survey. For in-vitro validation, 121 anonymous high-resolution ERM fundus images were analyzed with a 3D display coupled with the algorithm. The SSIM and PSNR of the model were 36.45±4.90 and 0.91±0.05, respectively, which indicated considerable improvements in image sharpness, brightness, and contrast. Visible ERM size and color contrast ratio were significantly enhanced in optimized images in the in-vitro studies. The results demonstrate that the developed algorithm can perform digital image enhancement effectively and has promise in the real-time applications during the 3D heads-up vitreoretinal surgeries. 2026 159 169 4053 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.210210 https://www.americaspg.com/journal/3/article/4053 https://www.americaspg.com/storage/31756596358.pdf