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Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network
Computational Intelligence and Neuroscience ( IF 3.120 ) Pub Date : 2021-06-30 , DOI: 10.1155/2021/1823930
Hongchang Wu 1
Affiliation  

The texture image decomposition of porcelain fragments based on convolutional neural network is a functional algorithm based on energy minimization. It maps the image to a suitable space and can effectively decompose the image structure, texture, and noise. This paper conducts a systematic research on image decomposition based on variational method and compressed sensing reconstruction of convolutional neural network. This paper uses the layered variational image decomposition method to decompose the image into structural components and texture components and uses a compressed sensing algorithm based on hybrid basis to reconstruct the structure and texture components with large data. In compressed sensing, to further increase each feature component, the sparseness of tight framework wavelet-based shearlet transform is constructed and combined with wave atoms as a joint sparse dictionary big data. Under the condition of the same sampling rate, this algorithm can retain more image texture details and big data than the algorithm. The production of big data that meets the characteristics of the background text is actually an image-based normalization method. This method is not very sensitive to the relative position, density, spacing, and thickness of the text. A super-resolution model for certain texture features can improve the restoration effect of such texture images. And the dataset extracted by the classification method used in this paper accounts for 20% of the total dataset, and at the same time, the PSNR value of 0.1 is improved on average. Therefore, taking into account the requirements for future big data experimental training, this article mainly uses jpg/csv two standardized database datasets after segmentation. This dataset minimizes the difference between the same type of base text in the same period to lay the foundation for good big data recognition in the future.

中文翻译:

基于卷积神经网络的瓷器碎片纹理图像分类方法

基于卷积神经网络的瓷片纹理图像分解是一种基于能量最小化的函数算法。它将图像映射到合适的空间,可以有效分解图像结构、纹理和噪声。本文对基于变分法的图像分解和卷积神经网络的压缩感知重构进行了系统的研究。本文采用分层变分图像分解方法将图像分解为结构分量和纹理分量,并采用基于混合基础的压缩感知算法对大数据进行结构分量和纹理分量的重构。在压缩感知中,为了进一步增加每个特征分量,构建了基于小波的剪切波变换的紧框架稀疏性,并将其与波原子结合作为联合稀疏字典大数据。在相同采样率的条件下,该算法比算法能够保留更多的图像纹理细节和大数据。符合背景文本特征的大数据的产生,实际上是一种基于图像的归一化方法。该方法对文本的相对位置、密度、间距和粗细不是很敏感。针对某些纹理特征的超分辨率模型可以提高此类纹理图像的恢复效果。并且本文采用的分类方法提取的数据集占总数据集的20%,同时PSNR值平均提高了0.1。因此,考虑到未来大数据实验训练的需求,本文主要使用分割后的jpg/csv两种标准化数据库数据集。该数据集最大限度地减少了同一时期同类型基础文本之间的差异,为未来良好的大数据识别奠定了基础。
更新日期:2021-06-30
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