Dynamic multi-exposure high dynamic range (HDR) image reconstruction remains challenging because it must preserve globally consistent luminance and structure while recovering fine-grained local textures from low dynamic range (LDR) inputs corrupted by saturation, under-exposure, and motion-induced artifacts. Existing CNN-based methods are effective at local detail restoration but remain limited in global context modeling, whereas Transformer-based methods improve long-range interaction but can still weaken local-detail refinement. Current hybrid designs suggest that the two representation types are complementary, but they do not fully address branch specialization, cross-branch collaboration, and local-feature reliability control. To address this gap, we propose a dual-branch Transformer-CNN framework with a base branch built on Window-based Residual Transformer Blocks (WRTBs), a detail branch equipped with Detail-Aware Gating (DAG) for reliability-aware local refinement, and Bidirectional Cross-Branch Fusion (BCBF) for stage-wise collaboration between the two branches. Experiments on Kalantari17, the Tel benchmark, and Challenge123 show that the proposed design remains competitive on the standard benchmark, achieving the best HDR-VDP2 and tied best with $\mu$-SSIM on Kalantari17, while yielding clearer gains on the more challenging Tel and Challenge123 benchmarks.