The existing image processing methods had aimed at the problems of blurred image reconstruction, large noise, and poor visual perception. The improved image super-resolution algorithm based on mixed deep convolutional networks is proposed in the paper. Firstly, the proposed method can shrink the low-resolution image to the specified size in upsampling phase. Secondly, it can extract features from low-resolution images. It sends the extracted initial features into the convolutional coding and decoding structure for image features. Thirdly, the feature extraction and calculation in high-dimensions are performed using dilated convolution in reconstruction layer. The high-resolution image had been reconstructed. The proposed method had been compared with state-of-arts on Set5, Set14, BSD100, and Urban100 datasets. The experimental results can show that the Peak Signal-to-Noise Ratio is increased by some ranges, and the Structural Similarity is increased by some effective percentage points.

现有的图像处理方法针对的是图像重建模糊、噪声大、视觉感知差的问题。论文提出了一种基于混合深度卷积网络的改进图像超分辨率算法。首先，所提出的方法可以在上采样阶段将低分辨率图像缩小到指定的尺寸。其次，它可以从低分辨率图像中提取特征。它将提取的初始特征送入图像特征的卷积编解码结构中。第三，在重建层使用扩张卷积进行高维特征提取和计算。高分辨率图像已被重建。已将所提出的方法与 Set5、Set14、BSD100 和 Urban100 数据集上的最新技术进行了比较。