当前位置: X-MOL 学术Signal Image Video Process. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
ECANet: enhanced context aggregation network for single image dehazing
Signal, Image and Video Processing ( IF 2.3 ) Pub Date : 2022-05-24 , DOI: 10.1007/s11760-022-02252-w
Zhigao Cui , Nian Wang , Yanzhao Su , Wei Zhang , Yunwei Lan , Aihua Li

Image dehazing is an important problem since computer recognition requires high-quality inputs. Recently, many researches tend to build an end-to-end multiscale network to restore haze-free images. But unfortunately, existing multiscale networks tend to recover under-dehazed results due to inefficient feature extraction. To solve the problem, we propose an enhanced context aggregation network for single image dehazing named ECANet. Based on encoder–decoder structure, the ECANet improves feature representation by three feature aggregation blocks (FABs) on each scale. The FAB is a new efficient feature extraction module, which adequately extracts content features and style features due to the difference receptive field between dilated convolution and ordinary convolution. To better fuse these complementary features, we combine spatial and channel attention mechanism to each FAB. After the decoding process, we also adopt an enhancing block to further refine image details under the supervision of clear references. The experimental results show that the proposed ECANet performs better than state-of-the-art dehazing methods, which recovers clear images with discriminative texture and natural color.



中文翻译:

ECANet:用于单幅图像去雾的增强上下文聚合网络

图像去雾是一个重要的问题,因为计算机识别需要高质量的输入。最近,许多研究倾向于建立端到端的多尺度网络来恢复无雾图像。但不幸的是,由于特征提取效率低下,现有的多尺度网络往往会恢复去雾不足的结果。为了解决这个问题,我们提出了一种增强的上下文聚合网络,用于名为 ECANet 的单图像去雾。基于编码器-解码器结构,ECANet 通过每个尺度上的三个特征聚合块 (FAB) 改进特征表示。FAB是一种新的高效特征提取模块,由于空洞卷积和普通卷积之间的感受野不同,它可以充分提取内容特征和风格特征。为了更好地融合这些互补的特征,我们将空间和通道注意力机制结合到每个 FAB。在解码过程之后,我们还采用增强块在清晰参考的监督下进一步细化图像细节。实验结果表明,所提出的 ECANet 的性能优于最先进的去雾方法,后者恢复了具有辨别纹理和自然色彩的清晰图像。

更新日期:2022-05-24
down
wechat
bug