基于自纠正机制的动态纹理合成模型

动态纹理是计算机视觉中的动态模型之一，在空间范围内具有统计平稳性，在时间维度上具有随机重复性。动态纹理合成的目标是生成与给定纹理在视觉上相似的图像。在进行动态纹理合成时，回归预测误差积累是导致纹理质量下降的一个关键问题。为此，本文提出一种基于自纠正机制的动态纹理合成模型。利用清晰度、结构相似性、光流等指标来确定优化数据范围，并找到优化极值点。通过自纠正机制，将原始数据替换为优化数据，并将优化数据用于回归预测。最后，利用卷积自编码器将预测数据重构为高维的动态纹理视频帧。在DynTex数据库上进行实验，并与几种典型的动态纹理合成模型进行比较。实验结果表明，用该模型合成的动态纹理视频帧与真实视频帧计算得到的MSE(Mean Square Error)数值更小，PSNR(Peak Signal to Noise Ratio)和SSIM(Structural SIMilarity)的数值更大。它解决了动态纹理合成中出现的残影、模糊、噪声等问题，从而能够生成视觉效果更好并且更长的动态纹理。同时，验证了所提出的建模方法的有效性。

Dynamic Texture Synthesis Model Based on Self-correction Mechanism

Dynamic texture is one of the dynamic models in computer vision. It has statistical stationarity in the spatial range and random repetition in the time dimension. The goal of dynamic texture synthesis is to generate an image that is visually similar to a given texture. When performing dynamic texture synthesis, the accumulation of regression prediction errors is a key issue of leading to the degradation of texture quality. Therefore, this paper proposes a dynamic texture synthesis model based on self-correction mechanism. The model uses indicators such as clarity, structural similarity, and optical flow to determine the optimal data range and finds the optimal extreme point. Through the self-correction mechanism, the original data is replaced with optimized data, and the optimized data is used for regression prediction. Finally, a convolutional autoencoder is used to reconstruct the prediction data into high-dimensional dynamic texture video frames. Experiments  are conducted on the DynTex database and the model proposed in this paper is compared with several typical dynamic texture synthesis models. The experimental results show that, compared with other models, the Mean Square Error (MSE) value calculated by the dynamic texture video frame and the real video frame synthesized by this model is smaller, and the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) are larger. It solves the problems of residual image, blur, and noise in dynamic texture synthesis, so as to generate a better visual effect and longer dynamic texture sequence. At the same time, the effectiveness of the proposed modeling method is verified.