基于深度学习的单幅图像超分辨率增强算法研究

Ming Han, Han Liu
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引用次数: 0

摘要

针对传统增强算法中边缘保护指数低的问题,提出了一种基于深度学习的单幅图像超分辨率增强算法。利用高斯函数调制的正弦二维变换函数提取单幅图像的超分辨率特征。采用局部拉普拉斯滤波对单幅图像的超分辨率进行预处理,并引入深度学习方法对单幅图像的超分辨率进行增强。实验结果表明,改进后的方法具有较高的边缘保护指数,能有效提高增强精度,具有一定的优势。
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Research on single image super resolution enhancement algorithm based on deep learning
To solve the problem of low edge protection index in traditional enhancement algorithm, a single image super-resolution enhancement algorithm based on deep learning is proposed. The super-resolution feature of a single image is extracted by the sinusoidal two-dimensional transform function modulated by Gaussian function. The local Laplacian filter is used to preprocess the super-resolution of a single image, and the deep learning method is introduced to enhance the super-resolution of a single image. The experimental results show that the improved method has higher edge protection index, can effectively improve the enhancement accuracy, and has certain advantages.
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