论文标题
低光图像增强的M-NET+的一半小波关注
Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement
论文作者
论文摘要
低光图像增强功能是一项计算机视觉任务，它将黑暗图像加剧到适当的亮度。在图像恢复域中，它也可以看作是一个不适的问题。随着深度神经网络的成功，卷积神经网络超过了基于算法的方法，并成为计算机视觉领域的主流。为了提高增强算法的性能，我们根据改进的层次模型：M-NET+提出了图像增强网络（HWMNET）。具体而言，我们在M-NET+上使用半波注意块来丰富小波域的特征。此外，在定量指标和视觉质量方面，我们的HWMNET在两个图像增强数据集上具有竞争性能结果。源代码和预估计的模型可在https://github.com/fanchimao/hwmnet上找到。
Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an ill-posed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-based methods and become the mainstream in the computer vision area. To advance the performance of enhancement algorithms, we propose an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use a half wavelet attention block on M-Net+ to enrich the features from wavelet domain. Furthermore, our HWMNet has competitive performance results on two image enhancement datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/FanChiMao/HWMNet.
