In this article, a spatial gamut mapping algorithm (SGMA) based on the guided filter is proposed. In this SGMA, the guided filter is firstly used to decompose the luminance channel of input image into high-pass signal representing image details (or textures) and low-pass signal representing image edges (or contours). The low-pass luminance signal is combined with other channels (chroma and hue) to get base-layer image. After the gamut mapping of base-layer image, the enhanced high-pass luminance signal is added back to the gamut-mapped base-layer image for detail preservation. Finally, the detail-preserved image is mapped to the destination gamut once again. In this article, the properties of the proposed SGMA, which include the principle of the guided filter, the influences of different guidance images on gamut mapping results, the influences of different frequency-band-decomposition methods on gamut mapping results, and the influences of different guided-filtering parameters on detail preservation, are analyzed in detail. The results of psychophysical experiments indicate that the proposed SGMA is better at detail preservation than pointwise GMAs, and has good performance on preference and reproduction accuracy. More importantly, the halos-testing experimental results verify the advantage of the proposed SGMA in avoiding halo-artifacts.