基于卷积神经网络的图像超分辨率算法可以分为图像尺寸放大和图像细节恢复/增强两个步骤，在细节恢复过程中，卷积层直接从输入图像中学习特征并将该特征作为下一个卷积层的输入数据。为了加强输入图像和卷积层各通道图像的特征表达能力，提出了一种新的卷积神经网络算法，该算法对输入图像和通道图像进行选择性灰度变换而增强特征表达的能力。实验结果表明，在公共红外图像数据集和实验室采集的红外图像数据集上，所提方法的超分辨率重建效果均优于当前的几种典型算法，能够恢复的细节信息更多。

Image super-resolution algorithms based on convolution neural network can be classified into two steps: image size enlargement and image detail recovery/enhancement. During the detail recovery process, the convolution layer learns the feature directly from the input image and takes the feature as the input data of the next convolution layer. In this study, a novel convolution neural network algorithm is proposed to enhance the feature expression ability of input and channel images in convolution layers by the selective gray transformation of the input and channel images. The experimental results demonstrate that the super-resolution reconstruction effect of the proposed method is superior to several typical algorithms in both conventional infrared images and the infrared images collected from our laboratory, and the proposed method can be applied to recover more details.