{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evaluating-image-fusion-techniques-for","title":"Evaluating Image Fusion Techniques for Improved Low Light Surveillance","arxiv_id":null,"date":"2024-06-25","proceeding":"International Journal of Engineering Research in Computer Science and Engineering, International Conference on Computational Intelligence and communication Network 2024 6","authors":["Akshat Mishra","Nikhil Bathija"],"abstract":"There has been a surge of interest towards deep learning based, image fusion methods in recent years. Through the process of image fusion, complimentary information is extracted from images that have been captured by multiple sensors. Irrelevant characteristics are screened out and the remaining relevant information is combined to enrich the detail and quality of these images. In the context of low light, image fusion techniques face difficulties while preserving the details and diminishing the noise produced in the resulting fused image. This occurs mainly due to the lack of visibility caused by insufficient lighting. Such conditions severely impact the fused images generated by the model. This research paper aims to conduct a comparative analysis of several state-of-the-art, deep learning based image fusion models for low light surveillance applications. Additionally, our paper will investigate the merits and challenges corresponding to each method in the context of low light image fusion. The results of our comparative analysis revealed that ‘SwinFuse’ exhibited superior performance when compared with other methods in preserving image details and reducing noise in the fused images.","url_abs":"https://ijercse.com/evaluating-image-fusion-techniques.php","url_pdf":"https://ijercse.com/article/27%20June%202024%20IJERCSE.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"evaluating-image-fusion-techniques-for","repo_url":"https://github.com/DipeshK47/Fusion-of-Low-Light-Electro-Optical-and-Infrared-Images-using-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}