Super-Resolution Image Reconstruction Based on Feature Fusion Network
In addressing the challenges posed by insufficient feature utilization and relatively weak high-frequency detail recovery capabilities in existing super-resolution reconstruction algorithms,this paper introduces an innovative two-stage feature fusion network(TFFN)with a relatively low network complexity.The network employs a dense connection approach to utilize convolutional kernels of varying sizes for up and down-sampling,thereby enhancing the comprehensive utilization of features.Subsequently,a two-stage feature fusion module is designed by employing a phased feature fusion approach to help the network focus on the extraction of high-frequency features.Extensive experimentation substantiates the lightweight and efficient characteristics of TFFN.In comparison to representative super-resolution reconstruction methods,TFFN achieves a commendable balance between reconstruction quality and network complexity.
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