Smoke Removal and Image Enhancement of Laparoscopic Images by An Artificial Multi-Exposure Image Fusion Method

Smoke Removal and Image Enhancement of Laparoscopic Images by An Artificial Multi-Exposure Image Fusion Method | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Smoke Removal and Image Enhancement of Laparoscopic Images by An Artificial Multi-Exposure Image Fusion Method Muhammad Adeel Azam, Khan Bahadar Khan, Eid Rehman, Sana Ullah Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-975713/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2022 Read the published version in Soft Computing → Version 1 posted 5 You are reading this latest preprint version Abstract In laparoscopic surgery, image quality is often degraded by surgical smoke or by side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The degraded images complicate the work of the surgeons and may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global image contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing the Multi-Exposure Fusion (PASD-MEF) technique to enhance the local contrast of laparoscopic images for better visualization. The set of under-exposure level images are obtained from a single input blurred image by using gamma correction. Spatial linear saturation is applied to enhance image contrast and to adjust the image saturation. The Multi-Exposure Fusion (MEF) is used on a series of multi-exposure images to obtain a single clear and smoke-free fused image. MEF is applied by using adaptive structure decomposition on all image patches. Image entropy based on the texture energy is used to calculate image energy strength. The texture entropy energy determined the patch size that is useful in the decomposition of image structure. The proposed method effectively eliminate smoke and enhance the degraded laparoscopic images. The qualitative results showed that the visual quality of the resultant images is improved and smoke-free. Furthermore, the quantitative scores computed of the metrics: FADE, Blur, JNBM, and Edge Intensity are significantly improved as compared to other existing methods. Software Engineering Artificial multi-exposure fusion Smoke removal Laparoscopic Images Image fusion and en-hancement Full Text Cite Share Download PDF Status: Published Journal Publication published 11 Apr, 2022 Read the published version in Soft Computing → Version 1 posted Editorial decision: Major Revision 28 Nov, 2021 Reviews received at journal 25 Oct, 2021 Reviewers invited by journal 25 Oct, 2021 Editor assigned by journal 15 Oct, 2021 First submitted to journal 12 Oct, 2021 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-975713","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":58803234,"identity":"db0df8c7-746c-4c7d-85e0-b9a9ad84c408","order_by":0,"name":"Muhammad Adeel Azam","email":"","orcid":"","institution":"University of Genoa: Universita degli Studi di Genova","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Adeel","lastName":"Azam","suffix":""},{"id":58803235,"identity":"87f7b6c1-55fe-43e1-a61e-d8eb87a61b1b","order_by":1,"name":"Khan Bahadar 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