一种基于多尺度小波阈值和双边滤波的图像去噪方法--外文翻译.rar

Abstract: A novel image denoising method is proposed based on
multiscale wavelet thresholding (WT) and bilateral filtering (BF).
First, the image is decomposed into multiscale subbands by wavelet
transform. Then, from the top scale to the bottom scale, we
apply BF to the approximation subbands and WT to the detail
subbands. The filtered subbands are reconstructed back to approximation
subbands of the lower scale. Finally, subbands are
reconstructed in all the scales, and in this way the denoised image
is formed. Different from conventional methods such as WT and
BF, it can smooth the low-frequency noise efficiently. Experiment
results on the image Lena and Rice show that the peak signal-
to-noise ratio (PSNR) is improved by at least 3 dB and 0.7 dB
compared with using the WT and BF, respectively. In addition, the
computational time of the proposed method is almost comparable
with that of WT but much less than that of BF.
Key words: wavelet thresholding; bilateral filtering; multiscale;
image denoising

摘要：本文提出了一种基于多尺度小波阈值（WT）和双边滤波（BF）的图像去噪方法。首先，图像会利用小波变换被分解成多尺度子带。然后，从顶部规模向底部规模，我们将BF应用到近似子带而将WT应用到细节子带。过滤后的子带进行重构后返回到了较低规模的近似子带。最后，子带在所有规模的范围内被重建后，并用这种方式对形成的图像去噪。不同于使用WT和BF 的传统方法，它可以有效地对低频噪音进行去噪。从图像上的实验结果可以表明，莉娜 和 大米的信噪比（PSNR）对比使用了WT 和BF之后，分别提高了至少3分贝和0.7分贝。此外，该方法的计算时间几乎可以与使用WT的方法相媲美但是远小于使用BF的方法。