In this paper, we proposed a novel approach to the creation of immersive multimedia content. At its core, EDAVS harnesses the subtle inflexions of human speech, translating emotive cues into dynamic visual narratives and corresponding soundscapes. This amalgamation of auditory sentiment and visual splendour is predicated upon cutting-edge machine-learning algorithms capable of deep semantic and emotional analysis.

We study lightweight approximation of a fixed engineering enhancement operator while limiting changes to usable image regions. Training only on corrected images leaves the pipeline’s intermediate intervention decisions unused. We propose MinePD-Net, which jointly distills corrected RGB outputs and native glare and dark-noise masks from a fixed, non-neural teacher. Predicted masks gate a bounded residual over an input identity path, limiting correction amplitude where intervention scores are low. A region-balanced objective assigns teacher reconstruction and raw-image identity to disjoint teacher-effect and clean-core regions, while retaining the trade-off between native-mask fidelity and transition reconstruction. This design couples decision transfer with correction control in a compact depthwise-separable encoder–decoder. Evaluation on UCM-WFE uses 4888 paired 2688 × 1520 frames from four fixed cameras at a single underground working face, source-clip-separated testing, repeated training, and leave-one-camera-out validation. Relative to a backbone-matched direct regressor, MinePD-Net improves reference peak signal-to-noise ratio by 0.33 dB and reduces clean-core identity error and flow-aligned temporal error by 36.1% and 14.8%, respectively. At native resolution, the 25,600-parameter model reaches 26.68 dB reference PSNR and 0.9234 SSIM. Network-only FP16 inference delivers 26.2 fps on an RTX 4070 Ti (batch size one), while the measured single-stream gateway-to-detection path delivers 17.90 fps with 28.40% dropped frames at a 25 fps input rate. A frozen equipment detector evaluated against human annotations improves from 0.446 to 0.472 mean average precision across intersection-over-union thresholds of 0.50–0.95, with a paired gain of +0.026 (95% CI: +0.009 to +0.043). Reference and mask scores quantify fidelity to the teacher’s outputs and decisions, not recovery of independently known clean scenes. Input preservation and human-annotated detection assess complementary benefits.