This paper describes an approach for small infrared (IR) target detection using frequency-spatial cues. We model the background as spikes of the amplitude spectrum in the frequency domain. Target regions are highlighted through background suppression, and the suppression is realized via convoluting the amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale. A theoretical analysis of the convoluting process in the frequency domain is presented. We note that the high values are attributed to sharp gradients in the IR image. In order to uniformly highlight the target region, the proposed algorithm introduces cues of image segmentation in the spatial domain. Targets are completely preserved in the final result. An image database is built, which is used to test the proposed algorithm. Results show that our algorithm detects small IR targets effectively with a competitive performance over some state-of-the-art techniques, even for images with cluttered backgrounds. In addition, we show that it is able to detect multiple targets with varied sizes, which are challenges for existing algorithms.