Conventional compressive sensing (CS) aims at sparse signal recovery from the measurements with continuous values. Quantized CS (QCS) methods arise in digital implementations where quantization of the receiver data is performed prior to signal processing. The extreme case of QCS is the so-called 1-bit CS where each real-valued measurement maintains only the sign information with one bit. The 1-bit CS alleviates the burden of storage and transmission of large data volumes and reduces the cost of the analog-to-digital converter. Recently, the 1-bit CS has been successfully applied to inverse scattering and radar imaging. In high-resolution radar imaging scenarios, targets assume spatial extent and occupy clustering pixels. The real and imaginary components of a complex sparse signal are the projections of the same complex value onto two orthogonal axes and, therefore, share a joint sparsity pattern. In this paper, a new 1-bit CS algorithm, referred to as enhanced-binary iterative hard thresholding (E-BIHT), is proposed to improve quality of 1-bit radar imaging by exploiting the two-level block sparsity exhibited in the two properties of clustering and the joint sparsity pattern of the real and imaginary parts of the target image. Simulations and experimental results demonstrate that compared to commonly used 1-bit CS algorithms, the proposed E-BIHT provides more informative imaging resulting in higher target-to-clutter ratio.