Abstract:Image super-resolution reconstruction technology has always been a hot research direction in the field of computer vision. To improve the quality of reconstructed images, this paper proposes an upsampling technology based on content awareness for image reconstruction. The residual dense network is used as the backbone network, and the content awareness-based upsampling replaces the traditional sub-pixel convolution upsampling. In other words, in the stage of feature reconstruction, the convolution kernel will not share parameters in the entire feature map, but the neural network can generate a specific convolution kernel depending on the content of the feature map in each pixel. The algorithm reduces the number of parameters, thereby speeding up the network training speed. After multiple rounds of training and testing, the results show that the improved technology can yield a clearer reconstructed image and presents a great visual effect.