Optical coherence tomography angiography (OCTA) is an indispensable modality in ophthalmic imaging, providing high-resolution visualization of retinal microvasculature. Recently, deep learning approaches have been explored to reconstruct OCTA images; however, significant challenges persist, particularly the reliance on high-quality target data for model training, which is often impractical due to limitations in hardware and acquisition protocols. In this work, we present a novel pipeline for deep learning-based OCTA imaging from repeated OCT B-scans, circumventing the need for high-quality training labels. We introduce an Intra-View Enhancement (IVE) module together with a novel loss function Cross-View Matching (CVM) to improve the imaging. The proposed pipeline is evaluated on a local dataset, demonstrating a relative improvement of \(4.97\%\) and \(27.42\%\) in PSNR and CNR over state-of-the-art learning-based OCTA method respectively. Our results underscore the effectiveness and clinical viability of the proposed approach for OCTA images, highlighting its potential to advance imaging capabilities in challenging clinical environments.