Towards Robust Low Light Image Enhancement

TL;DR

This paper presents a supervised learning approach for enhancing dark, low-light images captured in real-world conditions, effectively addressing noise and color distortions without requiring RAW data.

Contribution

It introduces a simulation-based training method that improves low-light image enhancement, outperforming existing methods on standard datasets.

Findings

Outperforms state-of-the-art methods quantitatively

Achieves strong qualitative improvements in image reconstruction

Effectively handles noise and color shifts in low-light images

Abstract

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.