Full waveform inversion (FWI) and reverse time migration (RTM) have shown great potential in subsurface imaging, yet they remain challenged by migration artifacts and limited deep illumination. A novel PPS-DLI (progressive pseudo-label self-supervised deep learning inversion) framework is proposed that integrates deep learning with multi-scale waveform inversion and imaging. The approach introduces a progressive model-based pseudo-label self-supervised strategy that iteratively refines velocity models by leveraging predictions from previous network stages. Combined with a frequency-based multi-scale inversion scheme, the method enables robust background velocity reconstruction at low frequencies and detailed imaging at higher frequencies. Numerical experiments demonstrate that PPS-DLI significantly reduces the migration artifacts, provides additional structural information with better illumination, improves the deep layers and sub-salt imaging, and preserves amplitude fidelity for enhanced lithological interpretation. These advantages position PPS-DLI as a powerful tool for high-resolution and noise-resistant seismic imaging.