  Existing Video Restoration (VR) methods always necessitate the individual
deployment of models for each adverse weather to remove diverse adverse weather
degradations, lacking the capability for adaptive processing of degradations.
Such limitation amplifies the complexity and deployment costs in practical
applications. To overcome this deficiency, in this paper, we propose a
Cross-consistent Deep Unfolding Network (CDUN) for All-In-One VR, which enables
the employment of a single model to remove diverse degradations for the first
time. Specifically, the proposed CDUN accomplishes a novel iterative
optimization framework, capable of restoring frames corrupted by corresponding
degradations according to the degradation features given in advance. To empower
the framework for eliminating diverse degradations, we devise a Sequence-wise
Adaptive Degradation Estimator (SADE) to estimate degradation features for the
input corrupted video. By orchestrating these two cascading procedures, CDUN
achieves adaptive processing for diverse degradation. In addition, we introduce
a window-based inter-frame fusion strategy to utilize information from more
adjacent frames. This strategy involves the progressive stacking of temporal
windows in multiple iterations, effectively enlarging the temporal receptive
field and enabling each frame's restoration to leverage information from
distant frames. Extensive experiments demonstrate that the proposed method
achieves state-of-the-art performance in All-In-One VR.
