School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China.
Shanghai Film Academy, Shanghai University, Shanghai, China.
PLoS One. 2022 Aug 23;17(8):e0272398. doi: 10.1371/journal.pone.0272398. eCollection 2022.
Existing learning-based methods for low-light image enhancement contain a large number of redundant features, the enhanced images lack detail and have strong noises. Some methods try to combine the pyramid structure to learn features from coarse to fine, but the inconsistency of the pyramid structure leads to luminance, color and texture deviations in the enhanced images. In addition, these methods are usually computationally complex and require high computational resource requirements. In this paper, we propose an efficient adaptive feature aggregation network (EAANet) for low-light image enhancement. Our model adopts a pyramid structure and includes multiple multi-scale feature aggregation block (MFAB) and one adaptive feature aggregation block (AFAB). MFAB is proposed to be embedded into each layer of the pyramid structure to fully extract features and reduce redundant features, while the AFAB is proposed for overcome the inconsistency of the pyramid structure. EAANet is very lightweight, with low device requirements and a quick running time. We conducted an extensive comparison with some state-of-the-art methods in terms of PSNR, SSIM, parameters, computations and running time on LOL and MIT5K datasets, and the experiments show that the proposed method has significant advantages in terms of comprehensive performance. The proposed method reconstructs images with richer color and texture, and the noises is effectively suppressed.
现有的基于学习的低光图像增强方法包含大量冗余特征，增强后的图像缺乏细节且噪声较强。一些方法试图结合金字塔结构从粗到精学习特征，但金字塔结构的不一致性导致增强图像的亮度、颜色和纹理出现偏差。此外，这些方法通常计算复杂，需要高计算资源。在本文中，我们提出了一种用于低光图像增强的高效自适应特征聚合网络（EAANet）。我们的模型采用金字塔结构，包括多个多尺度特征聚合块（MFAB）和一个自适应特征聚合块（AFAB）。MFAB 被提出嵌入到金字塔结构的每一层，以充分提取特征并减少冗余特征，而 AFAB 则被提出用于克服金字塔结构的不一致性。EAANet 非常轻量级，设备要求低，运行时间快。我们在 LOL 和 MIT5K 数据集上与一些最先进的方法进行了广泛的比较，包括 PSNR、SSIM、参数、计算和运行时间，实验表明，该方法在综合性能方面具有显著优势。所提出的方法重建的图像具有更丰富的颜色和纹理，并且有效地抑制了噪声。
