Integrating Application of Artificial Intelligence and Digital Imaging Techniques in Television Documentary Production

In the context of simultaneous upgrading of ultra-high-definition production and immersive storytelling, the integration of artificial intelligence and digital imaging technology needs to be urgently carried out in TV documentaries to dispel the bottleneck of efficiency and expression in the traditional editing-color grading-special effects pass. In this paper, we propose a D-DocFusion model, which uses a bidirectional temporal-semantic codec Transformer to jointly align scripts, interview texts and multi-camera RAW images to generate an editable "narrative timeline map". Subsequently, the improved Hierarchical NeRF-Diffusion module was introduced, which realized 3D duplication and super-resolution redrawing of old film sources with a controlled diffusion process while maintaining photometric consistency. Then, Cross-Attention Motion Composer fuses semantic clips with camera motion vectors to automatically generate tilt-shift, time-lapse, and virtual aerial trajectories that meet the director's intent. Comparative tests on BBC Planet Earth footage and its own 12 TB documentary library showed that D-DocFusion improved overall editing-grading efficiency by 47%.