Image super-resolution reconstruction is an important task in the field of image processing technology, which can restore low resolution image to high quality image with high resolution. In recent years, deep learning has been applied in the field of image super-resolution reconstruction. With the continuous development of deep neural network, the quality of the reconstructed images has been greatly improved, but the model complexity has also been increased. In this paper, we propose two lightweight models named as MSwinSR and UGSwinSR based on Swin Transformer. The most important structure in MSwinSR is called Multi-size Swin Transformer Block (MSTB), which mainly contains four parallel multi-head self-attention (MSA) blocks. UGSwinSR combines U-Net and GAN with Swin Transformer. Both of them can reduce the model complexity, but MSwinSR can reach a higher objective quality, while UGSwinSR can reach a higher perceptual quality. The experimental results demonstrate that MSwinSR increases PSNR by $\mathbf{0.07dB}$ compared with the state-of-the-art model SwinIR, while the number of parameters can reduced by $\mathbf{30.68\%}$, and the calculation cost can reduced by $\mathbf{9.936\%}$. UGSwinSR can effectively reduce the amount of calculation of the network, which can reduced by $\mathbf{90.92\%}$ compared with SwinIR.
翻译:图像超分辨率重建是图像处理技术领域的一项重要任务,它可以以高分辨率将低分辨率图像恢复到高品质图像。近年来,在图像超分辨率重建领域应用了深层次学习。随着深神经网络的持续发展,重建后的图像质量已大大改善,但模型复杂性也有所提高。在本文中,我们建议了两个轻量模型,名为MSwinSR和基于Swin变异器的UGSwinSR。MSwinSR中最重要的结构称为多尺寸 Swin变异器(MSTB),主要包含四个平行的多头自留(MSA)块。UGSwinSR将U-Net和GAN与Swin变异器结合起来。两者可以降低模型的复杂性,但MSwinSR可以达到更高的客观质量,而UGSwinSR可以达到更高的感官质量。实验结果表明MSwinSR增加PSNR$+$\ma_ma_maxSwin9=0.07B} 与州制SwinI(MSA)模型中主要包含四个平行的多头自留区块。UWIN(MSA)块。U-Net和GAN)结合了U-Net(MSN)和Swin GAN) 。两者,两者的计算可以减少成本。Swin36的计算数量,可以降低。