基于压缩卷积神经网络的图像超分辨率算法

为了有效提高深度图像的分辨率,文中借鉴经典SqueezeNet网络结构,提出一种基于Fire Module的卷积神经网络模型。该算法实现了直接从低分辨率图像到高分辨率图像的映射和转化,其中Fire Module作为网络的非线性映射模块,在减少参数的同时可学习图像的深层特征。为了避免插值预处理,在网络的输出层引入反卷积层,实现3倍上采样和高分辨率图像的输出。实验表明,采用该基于Fire Module的卷积神经网络模型的反卷积算法得到的超分辨率图像细节更加丰富,客观指标PSNR值和SSIM值的评价也明显优于其他算法。

Image Super-resolution Algorithm Based on SqueezeNet Convolution Neural Network

In order to effectively improve the resolution of depth image, the study proposed a convolutional neural network model based on the Fire Module by referring to the classic SqueezeNet network structure. The proposed algorithm implemented mapping and transformation directly from low-resolution images to high-resolution images. As a nonlinear mapping module of the network, Fire Module learned the deep features of the image while reducing the parameters. To avoid interpolation preprocessing, a deconvolution layer was introduced in the output layer of the network to achieve a final 3 times up-sampling and high resolution image output. Experiments showed that the super-resolution image obtained by the deconvolution algorithm of the convolutional neural network model based on Fire Module was richer in detail, and the evaluation of objective index PSNR value and SSIM value was also superior to other algorithms.