Image deblurring based on multiple local residual connection attention network

Aiming at the problem that existing image deblurring algorithms based on convolutional neural network are not clear in the restoration of image texture details, an image deblurring algorithm based on multiple local residual connection attention network was proposed. Firstly, a convolutional layer was used to extract the shallow features. Secondly, a new multiple local residual connection attention module based on residual connection and parallel attentional mechanism was designed to eliminate the image blur and extract the context information. Moreover, a pairwise connection module based on dilated convolution was adopted to restore details. Finally, a convolutional layer was used to reconstruct the clear images. The experimental results show that the peak signal to noise ratio (PSNR) and structure similarity (SSIM) on GoPro data set are 31.83 dB and 0.927 5, respectively. Both qualitative and quantitative results show that the proposed method can effectively restore the texture details of blurred images, and the network performance is better than that of the comparison method.