This　paper　proposes　a　new　framework　for　capturing　large　and　complex　deformation　in　image　registration.　Traditionally,　this　challenging　problem　relies　firstly　on　a　preregistration,　usually　an　affine　matrix　containing　rotation,　scale,　and　translation　and　afterwards　on　a　nonrigid　transformation.　According　to　preregistration,　the　directly　calculated　affine　matrix,　which　is　obtained　by　limited　pixel　information,　may　misregistrate　when　large　biases　exist,　thus　misleading　following　registration　subversively.　To　address　this　problem,　for　two-dimensional　(2D)　images,　the　two-layer　deep　adaptive　registration　framework　proposed　in　this　paper　firstly　accurately　classifies　the　rotation　parameter　through　multilayer　convolutional　neural　networks　(CNNs)　and　then　identifies　scale　and　translation　parameters　separately.　For　three-dimensional　(3D)　images,　affine　matrix　is　located　through　feature　correspondences　by　a　triplanar　2D　CNNs.　Then　deformation　removal　is　done　iteratively　through　preregistration　and　demons　registration.　By　comparison　with　the　state-of-the-art　registration　framework,　our　method　gains　more　accurate　registration　results　on　both　synthetic　and　real　datasets.　Besides,　principal　component　analysis　(PCA)　is　combined　with　correlation　like　Pearson　and　Spearman　to　form　new　similarity　standards　in　2D　and　3D　registration.　Experiment　results　also　show　faster　convergence　speed.