Noise Reduction Using Multi-resolution Edge Analysis

In this paper, a new noise reduction algorithm is proposed. In general, an edge—high frequency information in an image—would be filtered or suppressed after image smoothing. The noise would be attenuated, but the image would lose its sharp information. This defect makes the post-processing harder. One new algorithm performs connectivity analysis on edge-data to make sure that only isolated edge information that represents noise gets filtered out, hence preserving the overall edge structure of the original image. The steps of new algorithm are as follows. First, find the edge from the noisy image by multi-resolution analysis. Second, use connectivity analysis to direct a mean filter to suppress the noise while preserving the edge information. In the first step, we propose a new algorithm to find edges in a very noisy image. The algorithm is based on the analysis of a group of multi-resolution images obtained by processing the original noisy image by different Gaussian filters. After applied to a sequence of images of the same scene but with different signal-noise-ratio (snr), this method is robust to remove noise and keep the edge. Also, through statistic analysis, there exists the regularity that the parameters of the algorithm would be constant with varying images under the same snr.