Evidence map›Paper›PMID 41627707›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

BrightVAE: luminosity enhancement in underexposed endoscopic images.

Farzaneh Koohestani, Zahra Nabizadeh, Nader Karimi, Shahram Shirani, Shadrokh Samavi

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Farzaneh KoohestaniDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Zahra NabizadehDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Nader KarimiDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Shahram ShiraniDepartment of Electrical and Computer Engineering, McMaster University, Hamilton, ON, L8S 4L8, Canada.
Shadrokh SamaviComputer Science Department, Seattle University, Seattle, WA, 98122, USA. samavishadro@seattleu.edu.ORCID http://orcid.org/0009-0000-2591-3453

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeLow-light endoscopic images often lack contrast and clarity, obscuring anatomical details and reducing diagnostic accuracy. This study develops a method to enhance image brightness and visibility, enabling clearer visualization of critical structures to support precise medical diagnoses and improve patient outcomes.

methodsTo specifically address nonuniform illumination, we propose BrightVAE, a model that uses a dual-receptive-field architecture to decouple global brightness correction from local texture preservation. Integrated attention-based modules (Attencoder and Attenquant) explicitly target and amplify underexposed regions while preventing over-saturation, thereby recovering human-evaluable details in shadowed areas. The model was trained and tested on a public endoscopic dataset, and its performance was evaluated against other techniques using quality metrics.

resultsThe model outperformed alternatives, improving PSNR by 3.252 units, structural detail by 0.045, and perceptual quality by 0.014 compared to the best model before us, achieving a PSNR of 30.576, SSIM of 0.879, and LPIPS of 0.133, ensuring superior visibility of shadowed regions.

conclusionThis approach advances endoscopic imaging by delivering sharper, reliable images, enhancing diagnostic precision in clinical practice. Improved visualization supports better detection of abnormalities, potentially leading to more effective treatment decisions and enhanced patient care.

Indexed as

EndoscopyImage EnhancementImage Processing, Computer-AssistedAlgorithmsHumansAttention mechanismsDiagnostic precisionEndoscopic imagingLow-light enhancement

Identifiers

PMID41627707

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