基于密集Inception的单图像超分辨率重建方法

近几年，基于卷积神经网络（CNN）的单图像超分辨率（SR）重建方法成为了主流。通常情况下，重建模型的网络层数越深，提取的特征越多，重建效果越好；然而随着网络层数的加深，不仅会出现梯度消失的问题，还会显著增加参数量，增加训练的难度。针对以上问题，提出了一种基于密集Inception的单图像SR重建方法。该方法引入Inception-残差网络（Inception-ResNet）结构提取图像特征，全局采用简化后的密集网络，且仅构建每一个模块输出到重建层的路径，从而避免产生冗余数据来增加计算量。在放大倍数为4时，采用数据集Set5测试模型性能，结果显示与超深卷积神经网络的图像超分辨率（VDSR）相比，所提方法的结构相似性（SSIM）高了0.013 6；与基于多尺度残差网络的图像SR（MSRN）相比，SSIM高了0.002 9，模型参数量少了78%。实验结果表明，所提方法在保证模型的深度和宽度的情况下，显著减少了参数量，从而降低了训练的难度，而且取得了比对比方法更好的峰值信噪比（PSNR）和SSIM。

Single image super-resolution reconstruction method based on dense Inception

In recent years， the single image Super-Resolution （SR） reconstruction methods based on Convolutional Neural Network （CNN） have become mainstream. Under normal circumstances， the deeper network layers of the reconstruction model have， the more features are extracted， and the better reconstruction effect is. However， as the number of network layers increases， the reconstruction model will not only have the vanishing gradient problem， but also significantly increase the number of parameters and increase the difficulty of training. To solve the above problems， a single image SR reconstruction method based on dense Inception was proposed. In the proposed method， the image features were extracted by introducing the Inception-Residual Network （Inception-ResNet） structure， and the simplified dense network was adopted globally. And only the path that each module outputs to the reconstruction layer was constructed， avoiding the increase of computation amount caused by the generation of redundant data. When the magnification was 4， the dataset Set5 was used to test the model performance. The results show that， the Structural SIMilarity （SSIM） of the proposed model is 0.013 6 higher than that of accurate image Super-Resolution using Very Deep convolutional network （VDSR）， and the proposed method has the SSIM 0.002 9 higher and the model parameters 78% smaller than Multi-scale residual Network for Image Super-Resolution （MSRN）. The experimental results show that， under the premise of ensuring the depth and width of the model， the proposed method significantly reduces the number of parameters and the difficulty of training. In the meantime， the proposed method can achieve better Peak Signal-to-Noise Ratio （PSNR） and SSIM than the comparison methods.