Histogram　equalization　(HE)　is　a　classic　and　widely　used　image　contrast　enhancement　algorithm　for　its　good　performance　and　high　efficiency.　However,　over-enhancement　caused　by　high　peak　in　the　histogram　affects　the　subjective　quality　of　the　image　processed　by　HE　to　a　large　extent.　In　this　paper,　a　detail　weighted　histogram　equalization　(DWHE)　method　is　proposed　based　on　a　novel　histogram　modification　(HM)　model　named　partial　statistic　and　global　mapping　(PSGM)　to　alleviate　high　peak　and　suppress　over-enhancement.　Moreover,　the　authors　implement　a　refined　version　of　gamma　correction　(GC)　named　texture　enhancement　function　(TEF)　on　high-frequency　images　to　reduce　the　noise　amplification　effect.　At　last,　the　authors　propose　a　novel　adaptively　weighted　pixel-level　image　fusion　method　to　further　reduce　the　phenomenon　of　over-enhancement　and　improve　brightness　distribution.　Both　subjective　and　quantitative　evaluations　are　conducted　on　images　containing　a　variety　of　scenes.　Compared　with　several　state-of-the-art　image　enhancement　methods,　the　proposed　framework　obtained　generally　the　best　performance　in　aspects　of　both　subjective　appearance　and　objective　evaluation　indices.　Therefore,　it　is　proved　that　the　proposed　methods　can　effectively　alleviate　the　over-enhancement,　enrich　image　details,　and　efficiently　improve　the　visual　quality　while　preserving　the　brightness　of　the　image.