Block-based texture synthesis method and device

Abstract

The invention discloses a block-based texture synthesis method and device. The method comprises the following steps of: searching an overlapped area of each candidate texture block in a candidate texture block set and synthesized texture blocks in a target texture map; marking the overlapped areas in the synthesized texture blocks with B1ov, marking the overlapped regions in the candidate texture blocks with B2ov, calculating the square and errors of every pixel value of the overlapped parts between the candidate texture blocks and a synthesized texture area, and calculating the multiplication dot and errors of every pair of corresponding pixels in the overlapped areas between the candidate texture blocks and the synthesized texture area; calculating the square and errors by adopting an integral image method, and calculating the multiplication dot and errors by adopting FFT (fast Fourier transform); determining optimal texture blocks according to SSD (sum-of-squared differences) corresponding to every candidate texture block; and synthesizing the optimal texture blocks and the synthesized texture blocks in the target texture map, and repeating the steps until the texture synthesis of the target texture map is finished. The method and device accelerate the texture synthesis speed.

Description

technical field [0001] The invention relates to the field of graphics and image processing, in particular to a block-based texture synthesis method and device. Background technique [0002] Texture Synthesis from Samples (TSFS) based on samples is a new direction of texture synthesis developed after texture mapping and procedural texture synthesis methods. The principle is: based on a given small area texture sample, according to the geometric shape of the object surface, a texture image of any size is generated by stitching together. [0003] Currently, there are two categories of TSFS techniques: point-based texture synthesis and block-based texture synthesis. Among them, the point-based texture synthesis method mainly uses the Markov Random Field (MRF, Markov Random Field) model for texture synthesis. The MRF model believes that the texture has local statistical characteristics, that is, any part of the texture can be composed of its surrounding parts (ie Neighborhood) ...

AI Technical Summary

Problems solved by technology

[0014] From the implementation process of the above-mentioned block-based texture synthesis method, it can be seen that the most time-consuming part is the SSD error calculation link. The complexity of the above-mentioned SSD error calculation method directly affects the speed of texture synthesis.

[0015] Aiming at the problem that the SSD error calculation in the block-based texture synthesis method is complex, resulting in slow texture synthesis speed, no effective solution has been proposed yet

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