基于深度卷积神经网络的红外图像超分辨率重建技术

由于器件及工艺等技术限制，红外图像分辨率相对可见光图像较低，存在细节纹理特征模糊等不足。对此，本文提出一种基于深度卷积神经网络（convolutional neural network，CNN）的红外图像超分辨率重建方法。该方法改进残差模块，降低激活函数对信息流影响的同时加深网络，充分利用低分辨率红外图像的原始信息。结合高效通道注意力机制和通道-空间注意力模块，使重建过程中有选择性地捕获更多特征信息，有利于对红外图像高频细节更准确地进行重建。实验结果表明，本文方法重建红外图像峰值信噪比（peak signal to noise ratio，PSNR）优于传统的Bicubic插值法以及基于CNN的SRResNet、EDSR、RCAN模型。当尺度因子为×2和×4时，重建图像的平均PSNR值比传统Bicubic插值法分别提高了4.57 dB和3.37 dB。

Limits on super resolution and how to break them

Owing to technical limitations regarding the device and process, the resolution of infrared images is relatively low compared to that of visible images, and deficiencies occur such as blurred textural features. In this study, we proposed a super-resolution reconstruction method based on a deep convolutional neural network (CNN) for infrared images. The method improves the residual module, reduces the influence of the activation function on the information flow while deepening the network, and makes full use of the original information of low-resolution infrared images. Combined with an efficient channel attention mechanism and channel-space attention module, the reconstruction process selectively captures more feature information and facilitates a more accurate reconstruction of the high-frequency details of infrared images. The experimental results show that the peak signal-to-noise ratio (PSNR) of the infrared images reconstructed using this method outperforms those of the traditional Bicubic interpolation method, as well as the CNN-based SRResNet, EDSR, and RCAN models. When the scale factor is ×2 and ×4, the average PSNR values of the reconstructed images improved by 4.57 and 3.37 dB, respectively, compared with the traditional Bicubic interpolation method.