Efficient Real-Time Raw-to-Raw Denoising for Extreme Low-Light Ultra HD Video on Mobile Devices

Recent advancements in deep neural networks (DNNs) have significantly improved visual quality of camera captures under low-light (<10lx) conditions. Yet, visual quality in extreme low-light (<1lx) remains inadequate. Existing DNN models are computationally intensive and suffer from large processing times, making them impractical for realtime enhancement of high-resolution videos. Consequently, Ultra HD (UHD) videos (4K/8K) captured in extreme lowlight environments exhibit elevated noise and diminished details. Developing DNN-based solutions for UHD video enhancement faces challenges including paired dataset creation, temporal consistency, and efficient deployment under strict latency (<33ms) and power constraints (<250mA for 30fps video). We present a comprehensive methodology for developing a real-time raw-to-raw denoising solution for UHD videos in extreme low-light, designed for seamless integration into existing Image Signal Processor (ISP) pipelines. Unlike ISP-replacement approaches, our solution enhances commercial camera stacks across sensor platforms. Our framework comprises: (1) diverse dataset creation methodology, (2) a low-complexity model architecture optimized for mobile compute elements, and (3) efficient training and post-training optimizations (reparameterization, restructuring, quantization) to meet latency constraints while ensuring high-quality outputs. The result is a power-efficient real-time raw-to-raw video denoiser that improves extreme low-light video quality while preserving downstream ISP behavior.