Adaptive Guided Upsampling for Low-light Image Enhancement

• URL: http://arxiv.org/abs/2511.16623v1
• Date: Thu, 02 Oct 2025 19:05:29 GMT
• Title: Adaptive Guided Upsampling for Low-light Image Enhancement
• Authors: Angela Vivian Dcosta, Chunbo Song, Rafael Radkowski,
• Abstract summary: We introduce Adaptive Guided Upsampling (AGU), an efficient method for upscaling low-light images.<n>AGU can render high-quality images in real time using low-quality, low-resolution input.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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