Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution algorithm.It uses the nonlocal mea...Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution algorithm.It uses the nonlocal mean filter as a prior step to produce a denoised image.The proposed algorithm is based on curvelet transform.It converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands.In parallel,we applied sparse representation with over complete dictionary for the denoised image.The proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher resolution.The experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced edges.The comparison study shows that the proposed super-resolution algorithm outperforms the state-of-the-art.The mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.展开更多
为提高地震数据压缩感知重构的信噪比和保真度,提出一种基于曲波变换的地震数据压缩感知重构算法。建立了地震数据压缩感知重构模型,分析了基于曲波变换稀疏表示的地震数据各尺度之间能量与熵的分布特性,结合分块压缩感知技术降低随机...为提高地震数据压缩感知重构的信噪比和保真度,提出一种基于曲波变换的地震数据压缩感知重构算法。建立了地震数据压缩感知重构模型,分析了基于曲波变换稀疏表示的地震数据各尺度之间能量与熵的分布特性,结合分块压缩感知技术降低随机观测的计算复杂度,利用曲波变换稀疏表示高频区域各尺度之间的相关性,设计了随信息熵变化的自适应双变量收缩阈值迭代重构的方法。实验结果表明,在相同的采样率下,该算法重构的地震数据峰值信噪比提高了1.5 d B以上,并且具有良好的细节信息保持能力。展开更多
文摘Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution algorithm.It uses the nonlocal mean filter as a prior step to produce a denoised image.The proposed algorithm is based on curvelet transform.It converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands.In parallel,we applied sparse representation with over complete dictionary for the denoised image.The proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher resolution.The experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced edges.The comparison study shows that the proposed super-resolution algorithm outperforms the state-of-the-art.The mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.
文摘为提高地震数据压缩感知重构的信噪比和保真度,提出一种基于曲波变换的地震数据压缩感知重构算法。建立了地震数据压缩感知重构模型,分析了基于曲波变换稀疏表示的地震数据各尺度之间能量与熵的分布特性,结合分块压缩感知技术降低随机观测的计算复杂度,利用曲波变换稀疏表示高频区域各尺度之间的相关性,设计了随信息熵变化的自适应双变量收缩阈值迭代重构的方法。实验结果表明,在相同的采样率下,该算法重构的地震数据峰值信噪比提高了1.5 d B以上,并且具有良好的细节信息保持能力。
