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Over the last few decades there were dramatic improvements in ultrasound imaging quality with the utilization of harmonic frequencies induced by both tissue and echo-contrast agents. The advantages of harmonic imaging cause rapid penetration of this modality to diverse clinical uses, among which myocardial perfusion determination seems to be the most important application. In order to effectively employ the information, comprised in the higher harmonics of the received signals, this information should be properly extracted. A commonly used method of harmonics separation is linear filtering. One of its main shortcomings is the inverse relationship between the detectability of the contrast agent and the axial resolution. In this paper, a novel, nonlinear technique is proposed for separating the harmonic components, contained in the received radio-frequency images. It is demonstrated that the harmonic separation can be efficiently performed by means of convex optimization. It performs the separation without affecting the image resolution. The procedure is based on the concepts of sparse signal representation in overcomplete signal bases. A special type of the sparse signal representation, that is especially suitable for the problem at hand, is explicitly described. The ability of the novel technique to acquire "un-masked," second (or higher) harmonic images is demonstrated in series of computer and phantom experiments.