Abstract:

In recent years,the single-image super-resolution (SISR) method using deep convolutional neural networks (CNN) has achieved remarkable results.The Pixel Attention Network(PAN) is one of the most advanced lightweight super-resolution methods,which can lead to a good reconstruction performance with a very small number of parameters.But the PAN is limited by the parameters of each module,resulting in slow model training and strict training conditions.To address these problems,this paper proposes a Lightweight Adaptive Weight learning Network (LAWN) for image super-resolution.The network uses multiple adaptive weight modules to form a non-linear mapping network,with each module extracting different levels of feature information.In each adaptive weight module,the network employs the attention branch and the non-attention branch to extract the corresponding information,and then the adaptive weight fusion branch is employed to integrate these two branches.Splitting and fusing the two branches with a specific convolutional layer greatly reduces the number of parameters of the attention branch and the non-attention branch,which helps the network to achieve a relative balance between the number of parameters and the performance.The quantitative evaluations on benchmark datasets demonstrate that the proposed LAWN reduces the number of model parameters and performs favorably against state-of-the-art methods in terms of both PSNR and SSIM.Experimental results show that this method can reconstruct more accurate texture details.The qualitative evaluations with better visual effects prove the effectiveness of the proposed method.

Key words: super-resolution, convolutional neural networks, deep learning, lightweight, adaptive weight