Exemplar based Regular Texture Synthesis Using LSTM

Abstract Exemplar based texture synthesis is an important technique for image processing and computer graph in texture mapping. So far, great achievements have been made in this field. However, both traditional and modern methods based on deep learning are making errors in synthesizing patterned texture due to the failure for catching the regularity of texture. To obtain a better synthesized result, a new framework for regular texture synthesis is proposed in this paper. Besides, we use recurrent neural network (RNN) of long-shot term memory (LSTM) to produce a regular texture based on exemplar. Our method can generate at any size texture without errors, which is an improvement for texture synthesis with deep learning techniques.Compared with traditionanl method as well as deep learning method, our method is obviously better in synthesizing regular texture.