Image　enhancement　plays　a　crucial　role　in　computer　vision　by　improving　visual　quality　while　minimizing　distortion.　Traditional　methods　enhance　images　through　pixel　value　transformations,　yet　they　often　introduce　new　distortions.　Recent　advancements　in　deep　learning-based　techniques　promise　better　results　but　challenge　the　preservation　of　image　fidelity.　Therefore,　it　is　essential　to　evaluate　the　visual　quality　of　enhanced　images.　However,　existing　quality　assessment　methods　frequently　encounter　difficulties　due　to　the　unique　distortions　introduced　by　these　enhancements,　thereby　restricting　their　effectiveness.　To　address　these　challenges,　this　paper　proposes　a　novel　blind　image　quality　assessment　(BIQA)　method　for　enhanced　natural　images,　termed　multi-scale　local　feature　fusion　and　global　feature　representation-based　quality　assessment　(MLGQA).　This　model　integrates　three　key　components:　a　multi-scale　Feature　Attention　Mechanism　(FAM)　for　local　feature　extraction,　a　Local　Feature　Fusion　(LFF)　module　for　cross-scale　feature　synthesis,　and　a　Global　Feature　Representation　(GFR)　module　using　Vision　Transformers　to　capture　global　perceptual　attributes.　This　synergistic　framework　effectively　captures　both　fine-grained　local　distortions　and　broader　global　features　that　collectively　define　the　visual　quality　of　enhanced　images.　Furthermore,　in　the　absence　of　a　dedicated　benchmark　for　enhanced　natural　images,　we　design　the　Natural　Image　Enhancement　Database　(NIED),　a　largescale　dataset　consisting　of　8,581　original　images　and　102,972　enhanced　natural　images　generated　through　a　wide　array　of　traditional　and　deep　learning-based　enhancement　techniques.　Extensive　experiments　on　NIED　demonstrate　that　the　proposed　MLGQA　model　significantly　outperforms　current　state-of-the-art　BIQA　methods　in　terms　of　both　prediction　accuracy　and　robustness.　©　1991-2012　IEEE.