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Adaptive Guided Upsampling for Low-light Image Enhancement

arXiv 2511.16623 ↗First posted 2 Oct 2025cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 42 of its 110 references that are in the map. First posted on arXiv 2 Oct 2025.

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References · 42

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30 more
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