Digital Image Correlation (DIC) models are directed by texture-awareness means and the guidance signal loses crucial, fine-grained detail in the process when naively downsampled to modulate the abrasive, semantic feature maps in the encoder’s deeper layers. While current state-of-the-art models like RAFT achieve high precision through iterative refinement, they, in addition, undergo significant computational latency, limiting their efficacy in real-time industrial monitoring. The proposed model makes use of a five-level U-Net backbone joined with a hierarchical decoder that creates four-channel displacement and uncertainty priors across three resolution scales (1/4, 1/2, and full scale), ensuring that the expansive path obtains geometrically founded signals to do away with the need for recurrent loops. Experimental validation on a structured dataset of 36,000 reference-deformed images shows that the proposed method offers a highly optimized accuracy-latency tradeoff. DNet-MRSP achieves a mean End-Point Error (EPE) of \(0.00302 \pm 0.00050\text { px}\), exceeding baseline designs such as Deep DIC \(\text {(EPE} = 1.97721\text { px)}\) and unconstrained optical flow models like RAFT-DIC (\(\text {EPE} = 4.42217\text { px}\)). The framework has a rapid feed-forward execution latency of \(2.23 \pm 1.70\text { ms}\), which results in an operational processing throughput of 448.3 frames per second. This profile offers a slight latency trade-off with traditional Deep DIC (\(1.75\text { ms}\)) that is totally justified by a significant leap in sub-pixel tracking accuracy.