The gradient operator is frequently used for detecting edges in digital images. Although it does enhance edges well, it also enhances noise well, making edge detection difficult, especially for computer vision systems. This paper introduces a simple improvement of the gradient operator called gradient propagation. The standard method of detecting edges with a gradient operator is to calculate the gradient at each pixel and then threshold the magnitude of that gradient. Gradient propagation does exactly the same thing except that before thresholding, it adds copies of the gradient vector at each pixel to an output gradient image along a line perpendicular to the gradient vector. The gradient sums turn out to be large along edges and small noise. After thresholding, strong image edges processed by gradient propagation come out as good as those from the gradient operator but weak edges are detected better and with substantially less noise. The paper compares the results of the Sobel operator and equivalent edges. Both techniques produce little noise when detecting strong edges, but for weak edges, gradient propagation shows substantially less noise.