Stereo　super-resolution　is　a　technique　that　utilizes　corresponding　information　from　multiple　viewpoints　to　enhance　the　texture　of　low-resolution　images.　In　recent　years,　numerous　impressive　works　have　advocated　attention　mechanisms　based　on　epipolar　constraints　to　boost　the　performance　of　stereo　super-resolution.　However,　techniques　that　exclusively　depend　on　epipolar　constraint　attention　are　insufficient　to　recover　realistic　and　natural　textures　for　heavily　corrupted　low-resolution　images.　We　noticed　that　global　self-similarity　features　within　the　image　and　across　the　views　can　proficiently　fix　the　texture　details　of　low-resolution　images　that　are　severely　damaged.　Therefore,　in　the　current　paper,　we　propose　a　stereo　cross　global　learnable　attention　module　(SCGLAM),　aiming　to　improve　the　performance　of　stereo　super-resolution.　The　experimental　outcomes　show　that　our　approach　outperforms　others　when　dealing　with　heavily　damaged　low-resolution　images.　The　relevant　code　is　made　available　on　this　link　as　open　source.　©　2023　IEEE.