Adaptive Guided Upsampling for Low-light Image Enhancement
This addresses the challenge of enhancing low-light images for applications like photography or surveillance, but it appears incremental as it builds on existing guided image methods.
The paper tackles the problem of upscaling low-light images by introducing Adaptive Guided Upsampling (AGU), which reduces noise and increases sharpness through multi-parameter optimization, demonstrating superiority over state-of-the-art methods in experiments.
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.