With traditional ghost imaging methods, the detection of the edge resulting from a poorly recovered image is difficult; therefore, this paper proposes an improved high-pass-filter-based ghost imaging method. Randomly generated grayscale images are subjected to a high-pass filter before being input into a spatial light modulator. The high-frequency components in different directions of the unknown object are recovered by the correlation operation. Subsequently, the edge image is restored by the corresponding reconstruction method according to the filtering method used and edge detection of the unknown object without pre-known object information is realized. The Kirsch filter and nonsubsampled contourlet transform (NSCT) are considered as examples to show the performance of the algorithm. Compared with traditional edge-detection ghost imaging methods, the edge image obtained using the proposed algorithm has subjectively better smoothness and higher definition. The edge signal-to-noise ratio and mean square error are optimized.