ERIENet: An Efficient RAW Image Enhancement Network under Low-Light Environment

TL;DR

ERIENet is a lightweight, real-time RAW image enhancement network that effectively utilizes green channel information and parallel multi-scale processing to improve low-light image quality efficiently.

Contribution

The paper introduces a novel parallel multi-scale architecture with green channel guidance, achieving high-speed low-light RAW image enhancement with fewer parameters.

Findings

Outperforms state-of-the-art methods in low-light RAW image enhancement.

Achieves over 146 FPS for 4K images on a single GPU.

Reduces computational costs with a novel channel-aware residual dense block.

Abstract

RAW images have shown superior performance than sRGB images in many image processing tasks, especially for low-light image enhancement. However, most existing methods for RAW-based low-light enhancement usually sequentially process multi-scale information, which makes it difficult to achieve lightweight models and high processing speeds. Besides, they usually ignore the green channel superiority of RAW images, and fail to achieve better reconstruction performance with good use of green channel information. In this work, we propose an efficient RAW Image Enhancement Network (ERIENet), which parallelly processes multi-scale information with efficient convolution modules, and takes advantage of rich information in green channels to guide the reconstruction of images. Firstly, we introduce an efficient multi-scale fully-parallel architecture with a novel channel-aware residual dense block to extract feature maps, which reduces computational costs and achieves real-time processing speed. Secondly, we introduce a green channel guidance branch to exploit the rich information within the green channels of the input RAW image. It increases the quality of reconstruction results with few parameters and computations. Experiments on commonly used low-light image enhancement datasets show that ERIENet outperforms state-of-the-art methods in enhancing low-light RAW images with higher effiency. It also achieves an optimal speed of over 146 frame-per-second (FPS) for 4K-resolution images on a single NVIDIA GeForce RTX 3090 with 24G memory.