<p>CEDFlow introduces a latent contour enhancement method into dark optical flow estimation and achieves advanced performance. Nevertheless, it largely focuses on addressing the motion boundary in a local manner. Unfortunately, it falls short in performance when addressing significant variations or large-scale degraded scenes. This paper introduces CEDFlow++, which features three innovative modules to address the key challenges of CEDFlow. Firstly, we introduce a decomposition-based feature encoder (DBFE), which captures both fine-grained and large-scale features through its local encoder and a uniquely designed sparse attention-based global encoder that suppresses noise and interference that only exist in the dark. Secondly, for reliable motion analysis, we propose a customized dual cost-volume reasoning (DCVR), which integrates important high-contrast feature correlations of the global cost volume into the local cost volume, effectively capturing salient yet holistic motion information while mitigating motion ambiguity caused by darkness. Importantly, we present a contour-guided attention (CGA) which enables context-adaptive extraction of contour features by modifying the sign properties of the Sobel kernel parameters in latent space, specifically targeting large-scale contours that are suitable for motion boundaries. Experimental results on the FCDN and VBOF datasets show that CEDFlow++ outperforms state-of-the-art methods in terms of the EPE index and produces more accurate and robust optical flow.</p>