Adaptive Low-light Image Enhancement Network Based on Contextual Attention
The images collected from low light environment suffer from various challenges including low visibility,poor con-trast,color distortion and noise,which significantly affect subsequent applications in real world.In order to improve the quality of the captured image in this case,a low-light image enhancement method is proposed to relieve the issues,which designs an adaptive context attention mechanism.Specifically,the method firstly performs preliminary feature extraction on images through convolution-al decoder,and obtains the features of four different context information.Then,it uses the feature enhancement module with pix-el-wise and channel-wise attention to refine the extracted features.Finally,the learned enhanced features are input into the adap-tive fusion adjustment module,which integrates the previous shallow features with the refined one.The experimental results show that the proposed method can achieve significant improvement on both metrics of subjective visual perception and objective evalua-tion indicators(PSNR,SSIM,MAE),comparing with recent solutions.
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