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In this paper, we propose a new algorithm for removing Gaussian noise from natural images while preserving important details for further processing. Similar patches appear frequently in an image. Our algorithm is based on this high redundancy in natural images. Our initial step is to perform an image segmentation by a constrained quad-tree-split-and-merge technique. Then for each patch, using an extended SSD, we identify all similar patches in the image and recalculate the patch by summing up a weighted copy of each similar patch. Since the mean of white noise is zero, the final image will be noise free and important details are preserved. Experimental comparisons of Wiener Filter, Wavelet Threshold Methods and our method will be carried out by using different types of images. The result shows that our method is more efficient in both noise removing and detail preserving.