Stereo　image　super-resolution　utilizes　the　cross-view　complementary　information　brought　by　the　disparity　effect　of　left　and　right　perspective　images　to　reconstruct　higher-quality　images.　Cascading　feature　extraction　modules　and　cross-view　feature　interaction　modules　to　make　use　of　the　information　from　stereo　images　is　the　focus　of　numerous　methods.　However,　this　adds　a　great　deal　of　network　parameters　and　structural　redundancy.　To　facilitate　the　application　of　stereo　image　super-resolution　in　downstream　tasks,　we　propose　an　efficient　Multi-Level　Feature　Fusion　Network　for　Lightweight　Stereo　Image　Super-Resolution　(MFFSSR).　Specifically,　MFFSSR　utilizes　the　Hybrid　Attention　Feature　Extraction　Block　(HAFEB)　to　extract　multi-level　intra-view　features.　Using　the　channel　separation　strategy,　HAFEB　can　efficiently　interact　with　the　embedded　cross-view　interaction　module.　This　structural　configuration　can　efficiently　mine　features　inside　the　view　while　improving　the　efficiency　of　cross-view　information　sharing.　Hence,　reconstruct　image　details　and　textures　more　accurately.　Abundant　experiments　demonstrate　the　effectiveness　of　MFFSSR.　We　achieve　superior　performance　with　fewer　parameters.　The　source　code　is　available　at　https://　github.　com/KarosLYX/MFFSSR.