Super-resolution reconstruction method of mine image based on online multi-dictionary learning
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摘要: 针对基于字典学习的方法在处理含有噪声且环境复杂的矿井图像时重建效果不佳的问题，提出了一种基于在线多字典学习的矿井图像超分辨率重建方法。该方法利用K-means聚类算法将图像训练集划分为多类图像，并针对不同类图像训练多组高低分辨率字典，提高字典对环境复杂图像的特征表示能力；根据图像非局部自相似性，引入非局部约束项进一步约束稀疏系数的解空间，并通过在线字典学习对多字典学习阶段的字典进行优化，提高稀疏系数求解的准确性，从而提高图像重建过程的抗噪声干扰能力。实验结果表明，该方法能够有效提高重建图像质量，抑制噪声引起的图像块效应和边缘锯齿效应，增强图像细节，具有更好的视觉效果。Abstract: Aiming at problem that dictionary learning method was poorly effective in reconstruction of mine image with noise and complex environment, a super-resolution reconstruction method of mine image based on online multi-dictionary learning was proposed. The method uses K-means clustering algorithm to divide image training set into multiple kinds of images, and trains multiple sets of high-resolution dictionaries and low-resolution dictionaries for different kinds of images, so as to improve feature representation ability of the dictionaries for complex environmental images. According to non-local self-similarity of image, non-local constraint is introduced to further constrain solution space of sparse coefficient, and online dictionary learning is used to optimize the dictionaries in the multi-dictionary learning stage, so as to improve accuracy of sparse coefficient solution and anti-noise interference ability of image reconstruction process. The experimental results show that the method can effectively improve quality of reconstructed image, suppress image blocking effect and edge jagged effect caused by noise, enhance image details, and achieve better visual effect.
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期刊类型引用(8)
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