{"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/flight-mode-on-a-feather-light-network-for","title":"FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement","arxiv_id":"2305.10889","date":"2023-05-18","proceeding":null,"authors":["Mustafa Ozcan","Hamza Ergezer","Mustafa Ayazaoglu"],"abstract":"Low-light image enhancement (LLIE) is an ill-posed inverse problem due to the lack of knowledge of the desired image which is obtained under ideal illumination conditions. Low-light conditions give rise to two main issues: a suppressed image histogram and inconsistent relative color distributions with low signal-to-noise ratio. In order to address these problems, we propose a novel approach named FLIGHT-Net using a sequence of neural architecture blocks. The first block regulates illumination conditions through pixel-wise scene dependent illumination adjustment. The output image is produced in the output of the second block, which includes channel attention and denoising sub-blocks. Our highly efficient neural network architecture delivers state-of-the-art performance with only 25K parameters. The method's code, pretrained models and resulting images will be publicly available.","url_abs":"https://arxiv.org/abs/2305.10889v1","url_pdf":"https://arxiv.org/pdf/2305.10889v1.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":"flight-mode-on-a-feather-light-network-for","repo_url":"https://github.com/aselsan-research-imaging-team/flight-net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"efficient-neural-network","task_name":"Efficient Neural Network"},{"task_slug":"image-enhancement","task_name":"Image Enhancement"},{"task_slug":"low-light-image-enhancement","task_name":"Low-Light Image Enhancement"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/low-light-image-enhancement-on-lol","task":"Low-Light Image Enhancement","dataset":"LOL","model":"FLIGHTNet","rank_in_archive_order":19,"of":40,"metrics":{"Average PSNR":"24.96","Number of params":"0.025","SSIM":"0.85"},"uses_additional_data":false},{"leaderboard":"/sota/low-light-image-enhancement-on-lolv2","task":"Low-Light Image Enhancement","dataset":"LOLv2","model":"FLIGHTNet","rank_in_archive_order":12,"of":12,"metrics":{"Average PSNR":"21.71","SSIM":"0.834"},"uses_additional_data":false},{"leaderboard":"/sota/low-light-image-enhancement-on-lolv2-1","task":"Low-Light Image Enhancement","dataset":"LOLv2-synthetic","model":"FLIGHTNet","rank_in_archive_order":9,"of":9,"metrics":{"Average PSNR":"24.92","SSIM":"0.93"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}