In　the　era　of　deep　learning,　it＇s　important　to　obtain　enough　qualified　data　for　model　training.　For　the　labor-intensive　and　time-consuming　characteristics　of　data　collection　and　annotation,　date　augmentation　has　been　widely　employed　in　deep　learning　methods　to　mitigate　over-fitting.　It　has　been　proven　to　be　one　of　the　most　effective　ways　to　ensure　the　performance　of　deep　models.　In　the　field　of　computer　vision,　the　principal　goal　of　data　augmentation　usually　is　enlarging　both　the　scale　and　diversity　of　the　dataset,　and　maintaining　the　quality　of　both　the　image　and　the　annotation　data　simultaneously.　Data　augmentation　methods　at　the　raw　input　data　level　are　intuitive　to　obtain　semantic　meaningful　images.　In　this　paper,　we　provide　a　brief　survey　on　some　data　augmentation　studies　conducted　at　image　level　and　briefly　categorize　the　studies　into　two　main　classes:　those　based　on　image　editing　and　those　based　on　image　generation.　We　also　make　an　analysis　of　the　semantic-preserving　considerations　within　data　augmentation　techniques.　©　2024　The　Authors.