Image-text　retrieval　is　a　fundamental　yet　challenging　task,　which　aims　to　bridge　a　semantic　gap　between　heterogeneous　data　to　achieve　precise　measurements　of　semantic　similarity.　The　technique　of　fine-grained　alignment　between　cross-modal　features　plays　a　key　role　in　various　successful　methods　that　have　been　proposed.　Nevertheless,　existing　methods　cannot　effectively　utilise　intra-modal　information　to　enhance　feature　representation　and　lack　powerful　similarity　reasoning　to　get　a　precise　similarity　score.　Intending　to　tackle　these　issues,　a　context-aware　Relation　Enhancement　and　Similarity　Reasoning　model,　called　RESR,　is　proposed,　which　conducts　both　intra-modal　relation　enhancement　and　inter-modal　similarity　reasoning　while　considering　the　global-context　information.　For　intra-modal　relation　enhancement,　a　novel　context-aware　graph　convolutional　network　is　introduced　to　enhance　local　feature　representations　by　utilising　relation　and　global-context　information.　For　inter-modal　similarity　reasoning,　local　and　global　similarity　features　are　exploited　by　the　bidirectional　alignment　of　image　and　text,　and　the　similarity　reasoning　is　implemented　among　multi-granularity　similarity　features.　Finally,　refined　local　and　global　similarity　features　are　adaptively　fused　to　get　a　precise　similarity　score.　The　experimental　results　show　that　our　effective　model　outperforms　some　state-of-the-art　approaches,　achieving　average　improvements　of　2.5%　and　6.3%　in　R@sum　on　the　Flickr30K　and　MS-COCO　dataset.　A　novel　context-aware　relation　enhancement　and　similarity　reasoning　model　is　proposed　to　achieve　precise　image-text　retrieval,　which　conducts　both　intra-modal　relation　enhancement　and　inter-modal　similarity　reasoning　while　considering　the　global-context　information.　image