A　digital　image　has　become　an　essential　carrier　of　information　transmission,　but　it　will　inevitably　be　destroyed　during　the　transmission　process.　For　image　denoising　or　image　colorization,　there　are　already　many　excellent　algorithms,　and　there　have　been　promising　developments　in　the　field　of　deep　learning.　However,　in　these　classic　methods,　few　algorithms　can　accomplish　image　denoising　and　colorization　simultaneously.　This　paper　proposes　a　plug　and　play　framework　to　solve　this　problem,　which　combines　the　advantages　of　generative　adversarial　networks　and　plug　and　play　framework.　Moreover,　we　design　a　lightweight　conditional　GAN　to　achieve　the　purpose　of　training　with　fewer　computing　resources.　Experiments　on　CFP　and　Landscape　datasets　show　that　this　method　can　effectively　complete　image　denoising　and　image　colorization　tasks.　©　2021　IEEE.