Sparse Representation Based Super-Resolution Algorithm using Wavelet Domain Interpolation and Nonlocal Means

G. Suryanarayana, R. Dhuli
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引用次数: 2

Abstract

In this correspondence, we propose a novel image resolution enhancement algorithm based on discretewavelet transform (DWT), stationary wavelet transform (SWT) and sparse signal recovery of the inputimage. The nonlocal means filter is employed in the preliminary denoising stage of the proposed method.The denoised input low resolution (LR) image is then decomposed into different frequency subbands byemploying DWT and SWT simultaneously. In parallel, the denoised LR image is subjected to a sparse signalrepresentation based interpolation. All the estimated high frequency subbands as well as the sparseinterpolated LR image are fused to generate a high resolution (HR) image by using inverse discrete wavelettransform (IDWT). Experimental results on various test images show the superiority of our method over theconventional and state-of-the-art single image super- resolution (SR) techniques in achieving the real timeperformance.
基于稀疏表示的小波域插值和非局部均值超分辨算法
在本文中,我们提出了一种基于离散小波变换(DWT)、平稳小波变换(SWT)和输入图像稀疏信号恢复的图像分辨率增强算法。该方法的初始去噪阶段采用非局部均值滤波。然后利用小波变换和小波变换同时将去噪后的输入低分辨率图像分解为不同的频率子带。同时,降噪后的LR图像进行基于稀疏信号表示的插值。利用反离散小波变换(IDWT)将所有估计的高频子带与稀疏插值后的LR图像融合,生成高分辨率图像。在各种测试图像上的实验结果表明,我们的方法在实现实时性方面优于传统的和最先进的单图像超分辨率(SR)技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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