The conventional method of performing spatial filtering of RGB images is to subject each plane to the same processing, usually convolution with a filter kernel. Filtering is commonly used in the processing of photographic or photo-realistic images to sharpen or blur images, and to produce aesthetically-pleasing effects. For image sharpening, the technique of subjecting each plane to the same processing produces objectionable color errors in some circumstances, and that techniques which convert the image to a color space that separates luminance from chrominance and performing the filtering only on the luminance component can produce better results. The problem with this approach has been the computational cost of making the transformation, first to the luminance- chrominance space, and back to RGB. This paper presents an algorithm which operates on an RGB image and provides results which are free from chromaticity changes. It achieves these results with fewer computations than filtering the luminance component in a luminance-chrominance color space. In fact, the computations required are usually simpler than processing each RGB plane.