In this paper we develop an optimization-based solution to the problem of distributed radar imaging using antennas with asynchronous clocks. In particular, we consider a distributed radar imaging MIMO system observing a sparse scene under an unknown, but bounded, delay between the transmitter and receiver clocks. Most existing approaches pose the problem as the recovery of a phase shift, leading to non-convex formulations. Instead, inspired by recent work in blind deconvolution, we exploit the realization that synchronization errors in the received data can be modeled as a convolution with an unknown 1-sparse delay signal to be estimated in addition to the image. Thus, we formulate a convex optimization problem that simultaneously recovers all the pair-wise drifts between transmit/receive pairs, as well as the sparse scene being imaged. We verify the validity and performance of our proposed model and recovery method through numerical simulations on synthetic data.