We　propose　a　simple　and　effective　attention　module,　called　Efficient　Channel-and-Coordinate　Attention　(ECCA),　which　can　be　applied　to　CNN-based　image　stitching　models.　Traditional　image　stitching　models　usually　use　deep　convolutional　neural　networks,　but　as　the　depth　increases,　the　information　capacity　of　the　model　also　increases,　leading　to　unstable　region　of　interest　and　degraded　feature　quality.　Moreover,　for　similar　features　in　different　positions　of　two　images,　the　model　may　mistakenly　regard　them　as　the　same　feature,　resulting　in　incorrect　stitching　results.　To　address　these　issues,　we　design　a　multi-head　attention　mechanism　that　combines　channels　and　coordinate　positions.　The　module　can　enable　the　network　to　focus　on　more　critical　information　among　the　numerous　input　information　and　extract　higher　quality　features.　Specifically,　the　module　consists　of　two　attention　heads:　local　channel　attention　head　and　coordinate　attention　head.　The　local　channel　attention　head　calculates　the　correlation　between　each　channel　and　its　adjacent　k　channels,　and　the　coordinate　attention　head　calculates　the　correlation　between　each　channel　and　the　global　channel　according　to　the　feature　position　in　the　image.　The　two　attention　heads　obtain　attention　weights　respectively,　and　multiply　them　with　the　original　features　to　obtain　the　final　output　features.　Our　ECCA　module　can　effectively　improve　the　performance　of　existing　state-of-the-art　deep　image　stitching　models.　On　the　WarpedCOCO　dataset,　we　apply　the　ECCA　module　to　VFISnet　and　LB-UDHN　networks,　respectively　improving　their　PSNR　values　by　0.63　and　1.92,　and　SSIM　values　by　5.7%　and　5.1%.　Code　is　available　at　https://github.com/chenwinkk/ECCA-Network　©　2023　IEEE.