We present a new method to derive a multiscale urban camouflage pattern from a given set of background image samples. We applied this method to design a camouflage pattern for a given (semi-arid) urban environment. We performed a human visual search experiment and a computational evaluation study to assess the effectiveness of this multiscale camouflage pattern relative to the performance of 10 other (multiscale, disruptive and monotonous) patterns that were also designed for deployment in the same operating theater. The results show that the pattern combines the overall lowest detection probability with an average mean search time. We also show that a frequency-tuned saliency metric predicts human observer performance to an appreciable extent. This computational metric can therefore be incorporated in the design process to optimize the effectiveness of camouflage patterns derived from a set of background samples.