Towards Robust Low Light Image Enhancement
This work addresses low-light image enhancement for practical applications like photography, but it is incremental as it builds on existing supervised learning approaches with simulation-based data generation.
The paper tackles the problem of enhancing dark images taken in dim environments by addressing color shifts and sensor noise, using a supervised learning method with a simulated imaging pipeline to generate datasets, and reports outperforming state-of-the-art methods quantitatively on standard datasets.
In this paper, we study the problem of making brighter images from dark images found in the wild. The images are dark because they are taken in dim environments. They suffer from color shifts caused by quantization and from sensor noise. We don't know the true camera reponse function for such images and they are not RAW. We use a supervised learning method, relying on a straightforward simulation of an imaging pipeline to generate usable dataset for training and testing. On a number of standard datasets, our approach outperforms the state of the art quantitatively. Qualitative comparisons suggest strong improvements in reconstruction accuracy.