如何有效地增强图像边缘信息，消除图像的噪声是图像处理和计算机视觉研究中的基本问题。利用多尺度二进小波变换，对2维噪声图像进行了研究，提出了能量回归尺度空间滤波方法。利用一些图像进行了数值实验，实验结果表明，能量回归尺度空间滤波法可以较好地保留图像边缘特征，较多地去除噪声。与典型的小波萎缩去噪方法：“硬阈值”滤波法、“软阈值”滤波法相比较，能量回归滤波算法的峰值信噪比（PSNR）提高了2~3dB，从而证实了能量回归尺度空间滤波方法具有良好的去噪性能。

Enhance edges of images efficiently and removal noises in images are fundamental issues in the field of image processing and computer vision. A rescaled power space filtering method is proposed in this paper using multi-scale dyadic wavelet transform for 2D noisy image. Some sample images are tested and results demonstrate that the rescaled power space filtering method can preserve edges features and reduces noises. Compared with the commonly-used wavelet hard-thresholding and soft-thresholding methods, rescaled power space filtering algorithm increases the peak signal to noise ratio (PSNR) by 2~3dB which indicates the superior denoising performance of the power space filtering method.