Toward General-Purpose Video Reconstruction Through Synergy of Grid-Splicing Diffusion and Large Language Models

Various forms of degradation, including noise, blur, and adverse weather conditions (e.g., rain, snow, and fog), significantly compromise video quality and system reliability across critical domains ranging from surveillance and medical imaging to entertainment. Previous research mainly focuses on network models tailored to specific degradation types, while recent unified frameworks and foundation models still face critical challenges in temporal consistency, automated degradation recognition, and detail preservation. Despite recent advances in foundation models, current approaches rely heavily on predefined degradation labels and remain focused on image-level operations, limiting their generalization to real-world scenarios and struggling with preserving fine-grained details. To address these challenges, we propose Grid Splicing Diffusion Model (GSDiff), a general framework for video reconstruction that leverages a novel grid splicing execution alongside instruction-tuned Large Language Model (LLM). GSDiff introduces three key innovative modules: (1) a LLM-driven degradation recognition module that enables automatic and fine-grained restoration guidance through zero-shot degradation analysis, (2) a Grid Splicing Module that organizes multiple frames into a unified grid structure to facilitate spatiotemporal feature processing, and (3) a Detail Preservation Module integrated with a Tail Refine Network to enhance fine-grained details during diffusion and post-processing. Extensive experiments demonstrate that GSDiff delivers state-of-the-art performance across a wide range of reconstruction tasks, including deraining, desnowing, denoising, and deblurring, propelling advancements in medical diagnostics and smart city applications.