Towards Low Light Enhancement with RAW Images

TL;DR

This paper benchmarks the use of RAW images for low light enhancement, introduces a new evaluation framework, and develops a RAW-guided network that outperforms existing methods while only requiring RAW images during training.

Contribution

It presents the first benchmark for RAW image use in low light enhancement, introduces the Factorized Enhancement Model, and proposes REENet, a novel network leveraging RAW guidance during training.

Findings

RAW data linearity and exposure time are critical for enhancement performance.

REENet outperforms state-of-the-art sRGB-based methods.

Using RAW images only during training is effective for low light enhancement.

Abstract

In this paper, we make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement and develop a novel alternative route to utilize RAW images in a more flexible and practical way. Inspired by a full consideration on the typical image processing pipeline, we are inspired to develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors and provides a tool for exploring how properties of RAW images affect the enhancement performance empirically. The empirical benchmark results show that the Linearity of data and Exposure Time recorded in meta-data play the most critical role, which brings distinct performance gains in various measures over the approaches taking the sRGB images as input. With the insights obtained from the benchmark results in mind, a RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications in a way of using RAW images only in the training phase. REENet projects sRGB images into linear RAW domains to apply constraints with corresponding RAW images to reduce the difficulty of modeling training. After that, in the testing phase, our REENet does not rely on RAW images. Experimental results demonstrate not only the superiority of REENet to state-of-the-art sRGB-based methods and but also the effectiveness of the RAW guidance and all components.