Dynamic Video Denoising With Temporal Consistency Loss

The patent applies dynamics by transitioning from static image denoising to dynamic video denoising. The model processes multiple consecutive frames rather than single images, allowing the system to adapt to temporal variations and maintain stability across moving objects. This dynamic approach enables the denoising algorithm to distinguish between actual motion and artifacts, eliminating ghosting and shaking while preserving denoising effectiveness.

The patent implements continuity of useful action by processing consecutive video frames sequentially and maintaining temporal consistency. The model uses multiple adjacent frames to preserve temporal coherence, ensuring that denoised content remains stable across time. This continuous processing approach allows the system to maintain object identity and position across frames while removing noise, directly addressing the stability issue in dynamic videos.

The patent applies feedback by using consistency loss that compares denoised frames with their temporal neighbors. The model continuously adjusts its denoising output based on the consistency of surrounding frames, creating a feedback loop that refines clarity. This feedback mechanism ensures that denoised frames maintain high clarity by penalizing inconsistencies with adjacent frames, thereby eliminating ghosting effects while preserving noise reduction benefits.

The patent implements parameter changes by modifying the loss function parameters to include temporal consistency terms. The consistency loss incorporates parameters that measure temporal coherence between frames, allowing the model to adjust denoising strength and clarity preservation dynamically. This parameter adjustment enables the system to optimize both noise reduction and frame clarity simultaneously, preventing the ghosting artifacts that occur with traditional single-frame denoising.