This paper presents a feature-level defective pixel cluster (DPC) correction method specifically designed for dual-band infrared detectors. Due to inherent manufacturing limitations, antimonide-based type-II superlattice (T2SL) focal plane arrays (FPAs) commonly suffer from DPC issues. Existing DPC correction methods often fail to preserve dual-band imaging characteristics, leading to texture distortion and incomplete structure recovery. Inspired by infrared imaging mechanisms and image inpainting theory, we propose PRISM, a patch-based reconstruction framework that leverages inter-band structure migration with hierarchical feature decomposition, decoupling dual-band images into micro-textures, edge gradients, and spatial relationships. Leveraging this multi-level feature representation, we extract prior information to guide DPC correction. The correction process is formulated as a “structure-to-pixel” optimization problem, where an improved feature-guided patch search strategy effectively combines structure completion with texture reconstruction. Experimental results demonstrate that the proposed method not only recovers image content effectively but also faithfully preserves band-specific imaging characteristics. Furthermore, to facilitate quantitative evaluation, we construct a benchmark dataset containing simulated DPCs with corresponding ground truth. Comparative experiments on both our self-constructed dataset and public multimodal benchmarks confirm that PRISM achieves higher PSNR, SSIM, VIF, and CC metrics than state-of-the-art multimodal inpainting methods.