Advanced　deep　learning-based　Single　Image　Super-Resolution　(SISR)　techniques　are　designed　to　restore　high-frequency　image　details　and　enhance　imaging　resolution　through　the　use　of　rapid　and　lightweight　network　architectures.　Existing　SISR　methodologies　face　the　challenge　of　striking　a　balance　between　performance　and　computational　costs,　which　hinders　the　practical　application　of　SISR　methods.　In　response　to　this　challenge,　the　present　study　introduces　a　lightweight　network　known　as　the　Spatial　and　Channel　Aggregation　Network　(SCAN),　designed　to　excel　in　image　super-resolution　(SR)　tasks.　SCAN　is　the　first　SISR　method　to　employ　large-kernel　convolutions　combined　with　feature　reduction　operations.　This　design　enables　the　network　to　focus　more　on　challenging　intermediate-level　information　extraction,　leading　to　improved　performance　and　efficiency　of　the　network.　Additionally,　an　innovative　9　x　9　large　kernel　convolution　was　introduced　to　further　expand　the　receptive　field.　The　proposed　SCAN　method　outperforms　state-of-the-art　lightweight　SISR　methods　on　benchmark　datasets　with　a　0.13　dB　improvement　in　peak　signal-to-noise　ratio　(PSNR)　and　a　0.0013　increase　in　structural　similarity　(SSIM).　Moreover,　on　remote　sensing　datasets,　SCAN　achieves　a　0.4　dB　improvement　in　PSNR　and　a　0.0033　increase　in　SSIM.