In　image　restoration,　we　usually　assume　that　the　underlying　image　has　a　good　sparse　approximation　under　a　certain　system.　Wavelet　tight　frame　system　has　been　proven　to　be　such　an　efficient　system　to　sparsely　approximate　piecewise　smooth　images.　Thus,　it　has　been　widely　used　in　many　practical　image　restoration　problems.　However,　images　from　different　scenarios　are　so　diverse　that　no　static　wavelet　tight　frame　system　can　sparsely　approximate　all　of　them　well.　To　overcome　this,　recently,　Cai　et.　al.　(Appl　Comput　Harmon　Anal　37:89–105,　2014)　proposed　a　method　that　derives　a　data-driven　tight　frame　adapted　to　the　specific　input　image,　leading　to　a　better　sparse　approximation.　The　data-driven　tight　frame　has　been　applied　successfully　to　image　denoising　and　CT　image　reconstruction.　In　this　paper,　we　extend　this　data-driven　tight　frame　construction　method　to　multi-channel　images.　We　construct　a　discrete　tight　frame　system　for　each　channel　and　assume　their　sparse　coefficients　have　a　joint　sparsity.　The　multi-channel　data-driven　tight　frame　construction　scheme　is　applied　to　joint　color　and　depth　image　reconstruction.　Experimental　results　show　that　the　proposed　approach　has　a　better　performance　than　state-of-the-art　joint　color　and　depth　image　reconstruction　approaches.　©　2015,　Operations　Research　Society　of　China,　Periodicals　Agency　of　Shanghai　University,　Science　Press　and　Springer-Verlag　Berlin　Heidelberg.