"hrsf"

"The core contribution of this work lies in a hierarchical residual-driven modeling framework for image reconstruction. Specifically, the proposed method introduces (1) a structured residual decomposition strategy that separates coarse-scale prediction and fine-scale residual correction, (2) a spatially adaptive residual propagation mechanism guided by structural constraints, and (3) a dual-ranking consistency scheme for robust correspondence estimation. These components collectively address two fundamental challenges in image processing: structure preservation during reconstruction and mitigation of error propagation in iterative or spatially coupled models.Unlike conventional spatiotemporal fusion methods that are often tailored to specific sensors or application scenarios, the proposed framework is formulated from a general image processing perspective. The residual modeling, adaptive propagation, and consistency-constrained matching strategies can be directly extended to other image fusion, super-resolution, and restoration problems where multi-source or multi-resolution data are involved."