Study On Technology For Sample-Based Texture Synthesis

The goal of texture synthesis is to produce texture. It is an important research area in both computer graphics and image processing. Sample-based texture synthesis was a new technique proposed in recent years. It takes small texture image as input to synthesis same kind of texture but in arbitrary size. This technique is widely used in producing virtual reality scene and satisfies the great need in application areas such as movie industry and computer game. With the rapid development of computer 3D graphics ability and the great need of large size and high quality texture in recent years, sample-based texture synthesis became a hot research area.The goal of sample-based texture synthesis is high quality output image, synthesis speed that satisfies real time application and full automatic synthesis process. With the large amount work of researchers these years, sample-based texture synthesis technique has achieved much. The main challenge of this area currently lies in synthesis speed and the quality of synthesized image. In order to solve these problems, this thesis aims at fast and high quality texture synthesis algorithm.Main work of this thesis includes the following:(1) Global feature recognition and extraction for structured texture. A new structural pattern analysis method which is used to recognize feature point distribution pattern in structured texture is firstly proposed in this thesis. The method first clusters pixels in the texture according to the rule of neighborhood similarity, then selects a pixel group as the feature point group due to pixel neighborhood difference and analyses the distribution of pixels in this group, and finally, the method recognizes the feature point mesh. With the mesh, it is convenient to capture the global characteristics of texture. Thus the method can assist much in texture synthesis related applications. Many experiments shows that the method exhibit high generality to periodic and pseudo-periodic structured textures and can accurately recognize feature point mesh in a highly stochastic texture. Bases on the mesh statistic data, the method also quantizes macro scope randomness of texture. The quantized texture randomness parameter faithfully reflects the regularity of texture macro scope structure.(2) Feature point locating based texture synthesis algorithm. Bases on the traditional framework of patch-based texture synthesis, this thesis proposes texture synthesis algorithm using feature point locating. In the preprocessing phase, the algorithm uses structural pattern analysis on the sample image and performs global approximate feature point locating for the output image based on. the statistic data get. In the border fix phase, based on the traditional border cut technique, the algorithm proposes and uses a enhanced border fix method. The method splits the high and low frequency image component and deal with them separately to get smoother transition effect. It helps to improve image quality. It can be proved both in theory and in experiments that feature point locating assures the elimination of large scale structural error in the output image and at the same, searching space when performing patch matching is reduced more or less due to texture randomness. For most structured texture, searching space can be reduced not less than 1 to 2 decimal levels. This accelerates the texture synthesis process directly.(3) Structural coordinate based texture synthesis algorithm. Based on the traditional framework of pixel-based texture synthesis, this thesis proposes a texture synthesis algorithm which bases on structural coordinate matching. The algorithm firstly brings forward the concept of structural coordinate. In the pixel synthesis phase, differences of both structural coordinate and neighborhood color are used as matching principle. By the means of setting threshold value for structural coordinate matching, the algorithm offers the control on output texture randomness. Both theories and experiments shows that the using of structural coordinate matching remarkably improves the structure of synthesis texture and effectively makes up the lack in reproducing large scale texture characteristic. At the same time, searching space when performing global pixel matching decreases more or less due to texture randomness. This effectively accelerates the texture synthesis process.(4) Texture optimization algorithm using dynamic feature point matching. Bases on the optimization based texture synthesis algorithm, this thesis proposes a texture synthesis algorithm with feature point matching. In the preprocessing phase, the algorithm uses the periodic location of feature point as a rule to generate an optimized initial patch set. In the patch matching phase, the algorithm checks and assures the reasonable distance between feature points from different patches, and adjusts feature point mesh dynamically. Theories and experiments shows that initial patch set generated using feature point matching can accelerate the convergence of algorithm a lot. And feature point relative position check when matching patches can effectively assure the structure correctness of the output image.Main innovation points of the thesis are:(1) A firstly proposed structural pattern analysis method. It can effectively recognize and locate global characteristic of texture. It is a powerful assisting tool for conventional texture synthesis algorithms.(2) The proposed feature point locating based texture synthesis algorithm. The algorithm excludes the matching position which leads to structural errors in output image by means of feature point locating. This assures the correct structure of output image and greatly reduces the searching space when matching patches. It improves the synthesis speed directly.(3) In order to locate texture global characteristic, this thesis firstly brings forward the concept of structural coordinate, and then uses it in the framework of pixel-based texture synthesis. Through the matching of structural coordinate, global structural information of texture can be accurately passed to synthesis algorithm. This effectively improves output image structure and greatly decreases the searching space when matching pixels. It improves synthesis speed.