Image　super-resolution　(SR)　has　recently　gained　traction　in　various　fields,　including　remote　sensing,　biomedicine,　and　video　surveillance.　Nonetheless,　the　majority　of　advancements　in　SR　have　been　achieved　by　scaling　the　architecture　of　convolutional　neural　networks,　which　inevitably　increases　computational　complexity.　In　addition,　most　existing　SR　models　struggle　to　effectively　capture　high-frequency　information,　resulting　in　overly　smooth　reconstructed　images.　To　address　this　issue,　we　propose　a　lightweight　Progressive　Feature　Aggregation　Network　(PFAN),　which　leverages　Progressive　Feature　Aggregation　Block　to　enhance　different　features　through　a　progressive　strategy.　Specifically,　we　propose　a　Key　Information　Perception　Module　for　capturing　high-frequency　details　from　cross-spatial-channel　dimension　to　recover　edge　features.　Besides,　we　design　a　Local　Feature　Enhancement　Module,　which　effectively　combines　multi-scale　convolutions　for　local　feature　extraction　and　Transformer　for　long-range　dependencies　modeling.　Through　the　progressive　fusion　of　rich　edge　details　and　texture　features,　our　PFAN　successfully　achieves　better　reconstruction　performance.　Extensive　experiments　on　five　benchmark　datasets　demonstrate　that　PFAN　outperforms　state-of-the-art　methods　and　strikes　a　better　balance　across　SR　performance,　parameters,　and　computational　complexity.　Code　can　be　available　at　https://github.com/handsomeyxk/PFAN.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature　2025.