图像超分辨率的傅里叶-小波变换

S. Ashwini Devi, A. Vasuki
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引用次数: 4

摘要

从一个场景中拍摄的低分辨率图像可能包含肉眼几乎看不到的关键信息。超分辨率是将多个有噪声、模糊、低分辨率的图像组合成高质量、高分辨率图像的过程。通过配准,我们融合了在同一场景的不同时间、不同角度拍摄的图像。融合图像的恢复和去噪是实现超分辨率的关键。这里使用的多帧超分辨率算法是MForWarD。它是一个快速的两步算法。首先,基于傅里叶的维纳滤波产生一个清晰但有噪声的图像。下一步使用基于小波的去噪去除噪声伪影。将该算法应用于包括遥感图像在内的多个测试图像,并给出了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Super Resolution using Fourier-Wavelet transform
The low resolution images taken from a scene may contain crucial information that are barely visible to the eye. Super Resolution is the process of combining multiple noisy, blurry, low resolution images into a high quality, high resolution image. By registration, we fuse images taken at different times, at different angles of the same scene. Restoration and denoising of the fused images play a key role in Super Resolution. The multiframe Super Resolution algorithm applied here is MForWarD. It is a fast two step algorithm. First, Fourier-based Weiner filtering produces a sharp but noisy image. The next step uses Wavelet based denoising to remove noise artifacts. The algorithm is applied on several test images including remote sensing images and the results are presented.
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