Dual-lens (DL) cameras capture depth information, and hence enable several important vision applications. Most present-day DL cameras employ unconstrained settings in the two views in order to support extended functionalities. But a natural hindrance to their working is the ubiquitous motion blur encountered due to camera motion, object motion, or both. However, there exists not a single work for the prospective unconstrained DL cameras that addresses this problem (so called dynamic scene deblurring). Due to the unconstrained settings, degradations in the two views need not be the same, and consequently, naive deblurring approaches produce inconsistent left-right views and disrupt scene-consistent disparities. In this paper, we address this problem using Deep Learning and make three important contributions. First, we address the root cause of view-inconsistency in standard deblurring architectures using a Coherent Fusion Module. Second, we address an inherent problem in unconstrained DL deblurring that disrupts scene-consistent disparities by introducing a memory-efficient Adaptive Scale-space Approach. This signal processing formulation allows accommodation of different image-scales in the same network without increasing the number of parameters. Finally, we propose a module to address the Space-variant and Image-dependent nature of dynamic scene blur. We experimentally show that our proposed techniques have substantial practical merit.