School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China.
College of Science, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel). 2023 May 18;23(10):4854. doi: 10.3390/s23104854.
In the field of single-image super-resolution reconstruction, GAN can obtain the image texture more in line with the human eye. However, during the reconstruction process, it is easy to generate artifacts, false textures, and large deviations in details between the reconstructed image and the Ground Truth. In order to further improve the visual quality, we study the feature correlation between adjacent layers and propose a differential value dense residual network to solve this problem. We first use the deconvolution layer to enlarge the features, then extract the features through the convolution layer, and finally make a difference between the features before being magnified and the features after being extracted so that the difference can better reflect the areas that need attention. In the process of extracting the differential value, using the dense residual connection method for each layer can make the magnified features more complete, so the differential value obtained is more accurate. Next, the joint loss function is introduced to fuse high-frequency information and low-frequency information, which improves the visual effect of the reconstructed image to a certain extent. The experimental results on Set5, Set14, BSD100, and Urban datasets show that our proposed DVDR-SRGAN model is improved in terms of PSNR, SSIM, and LPIPS compared with the Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR models.
在单图像超分辨率重建领域，GAN 可以获得更符合人眼的图像纹理。然而，在重建过程中，很容易产生伪影、虚假纹理和重建图像与 Ground Truth 之间细节的较大偏差。为了进一步提高视觉质量，我们研究了相邻层之间的特征相关性，并提出了一种差分值密集残差网络来解决这个问题。我们首先使用反卷积层放大特征，然后通过卷积层提取特征，最后对放大前的特征和提取后的特征进行差值处理，使差值能够更好地反映需要注意的区域。在提取差分值的过程中，使用密集残差连接方法对每一层进行连接，使放大后的特征更加完整，从而使得到的差分值更加准确。接下来，引入联合损失函数来融合高频信息和低频信息，这在一定程度上提高了重建图像的视觉效果。在 Set5、Set14、BSD100 和 Urban 数据集上的实验结果表明，与 Bicubic、SRGAN、ESRGAN、Beby-GAN 和 SPSR 模型相比，我们提出的 DVDR-SRGAN 模型在 PSNR、SSIM 和 LPIPS 方面得到了改进。
