基于融合高频信息的红外图像超分辨率算法OA

To address the problems of insufficient temperature measurement accuracy and low resolution of current thermal imaging cameras,a temperature super-resolution model with an enhanced detail high-frequency component is developed by integrating a high-frequency filter block.The model first extracts the shallow features of a feature map through a convolutional layer.Second,a high-frequency filter block is introduced to highlight the high-frequency information,and jump joins are used to combine the raw data with high-frequency information.Finally,the temperature data are upsampled via convolution and pixel rearrangement,thus improving the resolution.This experiment is conducted on a self-constructed dataset,and the experimental results show that the enhanced detail high-frequency component model outperforms the fast super-resolution convolutional neural network and enhanced deep super-resolution network models.