We present a nov­el al­gorithm to de­noise deep Monte Carlo ren­der­ings, in which pixels con­tain mul­tiple col­our val­ues, each for a dif­fer­ent range of depths. Deep im­ages are a more ex­press­ive rep­res­ent­a­tion of the scene than con­ven­tion­al flat im­ages. However, since each depth bin re­ceives only a frac­tion of the flat pixel's samples, de­nois­ing the bins is harder due to the less ac­cur­ate mean and vari­ance es­tim­ates. Fur­ther­more, deep im­ages lack a reg­u­lar struc­ture in depth—the num­ber of depth bins and their depth ranges vary across pixels. This pre­vents a straight­for­ward ap­plic­a­tion of patch-based dis­tance met­rics fre­quently used to im­prove the ro­bust­ness of ex­ist­ing de­nois­ing fil­ters. We ad­dress these con­straints by com­bin­ing a flat im­age-space non-loc­al means fil­ter op­er­at­ing on pixel col­ours with a deep cross-bi­lat­er­al fil­ter op­er­at­ing on aux­il­i­ary fea­tures (al­bedo, nor­mal, etc.). Our ap­proach sig­ni­fic­antly re­duces noise in deep im­ages while pre­serving their struc­ture. To our best know­ledge, our al­gorithm is the first to en­able ef­fi­cient deep-com­pos­it­ing work­flows with de­noised Monte Carlo ren­der­ings. We demon­strate the per­form­ance of our fil­ter on a range of scenes high­light­ing the chal­lenges and ad­vant­ages of de­nois­ing deep im­ages.