Frames2Residual: Spatiotemporal Decoupling for Self-Supervised Video Denoising

Self-supervised video denoising methods typically extend image-based frameworks into the temporal dimension, yet they often struggleto integrate inter-frame temporal consistency with intra-frame spatialspecificity. Existing Video Blind-Spot Networks (BSNs) require noise in-dependence by masking the center pixel, this constraint prevents the useof spatial evidence for texture recovery, thereby severing spatiotempo-ral correlations and causing texture loss. To address this, we proposeFrames2Residual (F2R), a spatiotemporal decoupling framework thatexplicitly divides self-supervised training into two distinct stages: blindtemporal consistency modeling and non-blind spatial texture recovery.In Stage 1, a blind temporal estimator learns inter-frame consistencyusing a frame-wise blind strategy, producing a temporally consistent an-chor. In Stage 2, a non-blind spatial refiner leverages this anchor to safelyreintroduce the center frame and recover intra-frame high-frequency spa-tial residuals while preserving temporal stability. Extensive experimentsdemonstrate that our decoupling strategy allows F2R to outperform ex-isting self-supervised methods on both sRGB and raw video benchmarks.Code is available at https://github.com/m1NGGi/F2R.