Deep neural networks have significantly advanced video denoising. However, deploying accurate denoisers across platforms remains challenging, particularly under real-time constraints and with high-resolution inputs. While explicit motion estimation modules improve temporal alignment and enhance denoising quality, they are often omitted in lightweight models due to their computational burden. This paper investigates the trade-off between denoising quality and computational efficiency when using spatially coarse optical flow, and shows that reduced-resolution flow retains most temporal denoising benefits while significantly lowering computational cost. In addition, we introduce an inference-free imbalanced noise pre-training strategy that encourages effective temporal aggregation and complements coarse alignment without modifying the runtime architecture. The proposedLightweightVideoDenoiser (LVD) achieves state-of-the-art performance among lightweight models on RAW video denoising benchmarks while maintaining low latency suitable for real-time edge deployment. Our code is available athttps://github.com/camalab-ai/LVD

We introduce Frame Interpolation Pre-training (FIP), a simple learning technique for lifting deep image denoisers to video denoising with improved implicit temporal alignment. Modern video denoising networks typically rely on explicit motion estimation and alignment which are computationally intensive and harder to re-design and re-train, restricting their application scope and usability. Conversely, stacking frames and image denoisers, without incorporating explicit motion estimation modules, improves speed and benefits from a simpler design, thereby facilitating their generalizability to the video domain. However, it leads to lower accuracy due to suboptimal capture of temporal dependencies. To better leverage the adjacent frames in this setting and reduce the accuracy gap, we propose a novel training regime that divides the standard supervised training of the denoising task into two phases. In the initial phase, FIP guides the network to interpolate a fully masked central frame using only adjacent noisy input frames. In the subsequent phase, the pre-trained network is fine-tuned on denoising the central frame, now using all noisy input frames. Extensive diagnostics indicate that FIP-based networks provide better implicit motion estimation and temporal alignment. In effect, qualitative and quantitative evaluation on standard video denoising datasets with synthetic and real noise demonstrates that FIP consistently improves video denoising accuracy of motion-aware, video-lifted image denoisers without additional computational overhead during training and test time. Our code is available at https://github.com/camalab-ai/FIP.

Denoising videos in real-time is critical in many applications, including robotics and medicine, where varying-light conditions, miniaturized sensors, and optics can substantially compromise image quality. This work proposes the first video denoising method based on a deep neural network that achieves state-of-the-art performance on dynamic scenes while running in real-time on VGA video resolution with no frame latency. The backbone of our method is a novel, remarkably simple, temporal network of cascaded blocks with forward block output propagation. We train our architecture with short, long, and global residual connections by minimizing the restoration loss of pairs of frames, leading to a more effective training across noise levels. It is robust to heavy noise following Poisson-Gaussian noise statistics. The algorithm is evaluated on RAW and RGB data. We propose a denoising algorithm that requires no future frames to denoise a current frame, reducing its latency considerably. The visual and quantitative results show that our algorithm achieves state-of-the-art performance among efficient algorithms, achieving from two-fold to two-orders-of-magnitude speed-ups on standard benchmarks for video denoising.