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.
Abstract (translated)
许多图像处理任务中，RAW格式的图片相较于sRGB格式表现出更优的性能，尤其是在低光环境下的图片增强。然而，大多数现有的基于RAW数据进行低光增强的方法通常会按顺序处理多尺度信息，这使得构建轻量级模型和实现高速处理变得困难。此外，这些方法往往忽视了RAW图像绿色通道的优势，并未能充分利用绿色通道的信息来实现更好的重建性能。 为此，在这项工作中，我们提出了一种高效的RAW图像增强网络（ERIENet），该网络采用高效卷积模块并行地处理多尺度信息，并利用丰富的绿色通道信息指导图片的重建。具体来说： 1. 我们引入了一个高效的全并行多尺度架构和一个新颖的感知通道残差密集块，以提取特征图，这可以减少计算成本，实现实时处理速度。 2. 我们还引入了一条绿色通道引导分支，用于挖掘输入RAW图像中绿色通道内的丰富信息。这一做法在使用很少的参数和计算的情况下提升了重建结果的质量。 实验结果显示，在常用的低光图片增强数据集上，ERIENet相较于现有的最先进方法，在提升低光RAW图片质量方面表现出了更高的效率，并且对于4K分辨率的图像能够在单个NVIDIA GeForce RTX 3090（24G内存）显卡上以超过146帧每秒的速度运行。
URL
https://arxiv.org/abs/2512.15186
