Visualizing rapid biological dynamics like neuronal signaling and microvascular flow is crucial yet challenging due to photon noise and motion artifacts. Here we present a deep learning framework for enhancing the spatiotemporal relations of optical microscopy data. Our approach leverages correlations of mirrored perspectives from conjugated scan paths, training a model to suppress noise and motion blur by restoring degraded spatial features. Quantitative validation on vibrational calcium imaging validates significant gains in spatiotemporal correlation (2.2×), signal-to-noise ratio (9–12 dB), structural similarity (6.6×), and motion tolerance compared to raw data. We further apply the framework to diverse in viv o experiments from mouse cerebral hemodynamics to zebrafish cardiac dynamics. This approach enables the clear visualization of the rapid nutrient flow (30 mm/s) in microcirculation and the systolic and diastolic processes of heartbeat (2.7 cycle/s), as well as cellular and vascular structure in deep cortex. Unlike techniques relying on temporal correlations, learning inherent spatial priors avoids motion-induced artifacts. This self-supervised strategy flexibly enhances live microscopy under photon-limited and motion-prone regimes.