{"ID":2861684,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16623","arxiv_id":"2511.16623","title":"Adaptive Guided Upsampling for Low-light Image Enhancement","abstract":"We introduce Adaptive Guided Upsampling (AGU), an efficient method for upscaling low-light images capable of optimizing multiple image quality characteristics at the same time, such as reducing noise and increasing sharpness. It is based on a guided image method, which transfers image characteristics from a guidance image to the target image. Using state-of-the-art guided methods, low-light images lack sufficient characteristics for this purpose due to their high noise level and low brightness, rendering suboptimal/not significantly improved images in the process. We solve this problem with multi-parameter optimization, learning the association between multiple low-light and bright image characteristics. Our proposed machine learning method learns these characteristics from a few sample images-pairs. AGU can render high-quality images in real time using low-quality, low-resolution input; our experiments demonstrate that it is superior to state-of-the-art methods in the addressed low-light use case.","short_abstract":"We introduce Adaptive Guided Upsampling (AGU), an efficient method for upscaling low-light images capable of optimizing multiple image quality characteristics at the same time, such as reducing noise and increasing sharpness. It is based on a guided image method, which transfers image characteristics from a guidance im...","url_abs":"https://arxiv.org/abs/2511.16623","url_pdf":"https://arxiv.org/pdf/2511.16623v1","authors":"[\"Angela Vivian Dcosta\",\"Chunbo Song\",\"Rafael Radkowski\"]","published":"2025-10-02T19:05:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\",\"eess.IV\"]","methods":"[]","has_code":false}