While monocular depth estimation remains a primary hurdle in computer vision, this research presents a sophisticated hybrid framework designed to extract high-fidelity depth information from static 2D images. The core of this methodology lies in its dual-stream architecture: it synchronizes a global depth hypothesis generated via Deep Learning with a localized, edge-sensitive segmentation strategy. To ensure the system remains versatile across a spectrum of hardware from high-performance servers to resource-constrained mobile devices, this work implements a quality-scalable block partitioning scheme. By discretizing the image into adjustable blocks, the system can dynamically balance computational overhead against spatial precision. This process is deeply informed by the luminance channel's edge probability, which acts as a structural guide to ensure that depth transitions are mathematically anchored to the actual physical boundaries of objects. To bridge the gap between discrete block processing and a continuous, natural depth field, a guided bilateral filter is employed in the final stage. This specific refinement serves two purposes: it effectively dissolves 'staircase' or blocky artifacts resulting from the segmentation, while simultaneously acting as a 'boundary-lock' to preserve the crispness of foreground silhouettes. The resulting depth maps exhibit a granular level of detail, particularly at high-resolution block settings—providing the necessary structural accuracy for seamless 3D conversion, cinematic depth-of-field effects, and high-immersion Augmented Reality (AR) environments. GENERATION USING EDGE-CONDITIONED DEEP LEARNING PRIORS