In　order　to　enhance　the　de-noising　performance　of　wavelet　thresholding　de-noising　algorithm　and　increase　the　degree　of　similarity　between　the　original　signal　and　de-noising　signal,　this　paper　proposes　a　new　thresholding　function　with　adjustment　factors　based　on　wavelet　thresholding　de-noising　theory　introduced　by　D.　L.　Donoho　and　I.　M.　Johnstone.　First　continuity　and　higher　derivative　of　thresholding　function　can　be　obtained　by　setting　adjustment　factors,　second　the　difference　between　the　value　of　the　original　signal　and　the　one　quantized　is　unlimitedly　decreased,　finally,　according　to　the　noisy　signal　features　and　the　distributional　difference　of　noise　energy,　the　effective　thresholding　function　can　be　obtained　with　different　adjustment　factors.　Objective　assessment　parameters　of　de-noising　image　quality　are　calculated　from　the　experiment　of　Matlab　simulation,　which　suggests　that　the　impact　of　de-noising　algorithm　with　the　new　thresholding　function　is　better　than　the　traditional　soft　and　hard　thresholding　de-noising　algorithms,　furthermore,　which　lays　a　good　foundation　on　image　enhancement,　feature　extraction　and　edge　detection.