跨级特征自适应融合的暗光图像增强算法

Cross-level feature adaptive fusion network for low-light image enhancement

针对低照度环境下采集的图像存在亮度低、对比度低和视觉效果不佳等问题,提出一种跨级特征自适应融合的低照度图像增强算法。该算法首先结合分级采样和广域卷积搭建网络前端,生成大面积感受野的多尺度特征,使浅层信息被充分挖掘。其次引入多头转置注意力模块嵌于网络中部,计算通道间的互协方差以生成注意力映射,隐式地建立全局上下文信息关联。然后构建联合损失函数修正模型收敛方向,辅助模型从对比度和结构等方面进行优化,提高算法的鲁棒性。最后在LOL和LOLv2数据集上进行实验验证,实验结果表明,所提算法在峰值信噪比(PSNR)和结构相似性(SSIM)等客观指标上整体优于大部分先进算法,主观上图像亮度自然,噪声和伪影得到有效抑制。

Aiming at the problems of low brightness,low contrast and poor visual effect in images collected in low-light environment,a low-light image enhancement algorithm based on cross-level adaptive feature fusion is proposed.Firstly,a network frontend is built by combining hierarchical sampling and large receptive field convolution to generate multi-scale features of large-area receptive fields,so that shallow information mining can be fully carried out.Secondly,a multi-head transposed attention module embedded in the middle of the network is introduced,the cross-covariance between channels is calculated to generate attention maps,and global context information associations are implicitly established.Thirdly,a joint loss function is constructed to correct the convergence direction of the model,assist the model optimized from the perspective of contrast and structure,and improve the robustness of the algorithm.Relevant experiments are carried out on the LOL and LOLv2 datasets.The experimental results show that the proposed algorithm outperforms most advanced algorithms in terms of objective indicators such as peak signal-to-noise ratio(PSNR)and structural similarity(SSIM).Subjectively,the image brightness is natural and the noise is low,and artifacts are effectively suppressed.