Abstract: In the process of single image super-resolution reconstruction (SISR) based on learning from examples， the mapping relation was assumed one-to-one from a low-resolution (LR) input to a high-resolution (HR) image patch． But in fact，one LR patch may relate to many HR patches，and thus leads to matching errors． To solve the mismatch problem of restored patch，the probability model of LR patch topic pattern was derived to express new observation information for hidden topics in LR signals． Then a structure-aware recovery mechanism with topic differences and context maximum probability was proposed，and LR manifold description was formed by relating topic modes to LR neighbor contents． The HR signal was accurately distinguished and reconstructed from similar LR manifold signals via an adaptive selection of topic decision trees and regression matrix of nodes． The topic mo- del optimization experiment demonstrates that the peak signal-to-noise ratio (PSNR) of our topic constraint SISR method is improved by 0. 25dB compared to that of the decision tree based SISR algorithm without introducing hid- den topics． In the comparative experiment of five algorithms，the average PSNR value of our SISR approach is im- proved by 0. 92dB compared to that of the sparse dictionary based SISR method． So the introduced hidden topic in- formation and topic-manifold structure identification are feasible．

Key words: super-resolution reconstruction, topic probability model, structure perception, manifold constraint, node regression mapping