An image is often modeled as a product of two principal components: illumination and reflectance components. The former is related to the amount of light incident on the scene and the latter is associated with the scene characteristics. The images formed from the two components are referred to as the illumination and the reflectance images; both are called the intrinsic images of the original image. The illumination components of the images of a fixed scene vary from image to image, while the reflectance components of the images in principle remain constant. Both reflectance and illumination images have their own applications. Intrinsic image extraction has long been an important task for computer vision applications. However, this task is not at all simple because it is an illconditioned problem. The proposed approach convolves an input image with a prescribed set of derivative filters. The pixels of the derivative images are next classified as being reflectance or illumination according to three measures: chromatic, intensity contrast and edge sharpness, which are calculated in advance for each pixel from the input image. Finally, a de-convolution process is applied to the classified derivative images to obtain the intrinsic images. The results reveal the feasibility of the proposed technique in both rapidly and effectively decomposing intrinsic images from one single image.