多支路融合注意力机制的低光照图像增强

Low light image enhancement based on multi-branch fusion attention mechanism

低光照图像因对比度低、细节信息丢失、颜色失真以及噪声伪影等引起的过度曝光,质量差等问题,融合多支路和注意力机制,提出一种基于分解任务的多支路低光增强网络(MANet).首先低光输入图像经过VGG-19卷积对特征采集同时为了降低计算量,将普通卷积替换为可分离卷积的网络提取出边缘、纹理等有效特征.然后为了能够自适应对图像中不同区域进行自适应亮度增强和噪声伪影抑制,在增强模块中引入注意力机制,利用注意力机制来学习和设置不同权重信息,获取特征信息来增强.最后,为了进一步提高图像质量,在融合模块中使用多尺度特征融合,使得上下文信息得到进一步的融合和增强.实验结果表明,MANet能够自适应的提升图像亮度的同时降低图像的噪声和去除伪影,与GLADNet网络相比PSNR提高了7%,SSIM提高了3.3%.

A decomposition task-based multi-branch low-light enhancement network(MANet)is proposed for low-light images with poor quality due to overexposure caused by low contrast,loss of detail information,color distortion and noise artifacts,etc.,by fusing multi-branch and attention mechanisms.Firstly,the low-light input image is subjected to VGG-19 convolution for feature acquisition at the same time in order to reduce the computational effort,the normal convolution is replaced by a network with separable convolution to extract effective features such as edges and textures.Then in order to be able to adaptively perform adaptive brightness enhancement and noise artifact suppression for different regions in the image,an attention mechanism is introduced in the enhancement module,which is used to learn and set different weight information to obtain feature information for enhancement.Finally,in order to further improve the image quality,multi-scale feature fusion is used in the fusion module,which enables further fusion and enhancement of contextual information.The experimental results show that MANet can adaptively enhance image brightness while reducing image noise and removing artifacts,and improves PSNR by 7%and SSIM by 3.3%compared with GLADNet network.