<p>Considerable progress has been made in high dynamic range (HDR) image reconstruction from multi-exposure low dynamic range (LDR) frames in recent years. Despite achieving satisfactory HDR results while handling the misalignment among LDR frames, previous studies pay less attention to a prevalent but more crucial concern: the significant noise and motion blur that exist in multi-exposure frames captured by a handheld camera. To overcome these extensive and challenging corruptions, we achieve the HDR imaging task from two key aspects: First, due to the absence of related datasets, previous learning-based methods struggle with performing high-quality HDR imaging when the input LDR frames are confronted with real-world image noise and motion blur. Recognizing the importance of this aspect, we propose the first available realistic dataset based on the real-world burst imaging pipeline for training and evaluating different methods in the joint HDR imaging, denoising, and deblurring task. Second, due to the corruption-insensitive of previous network architectures, we propose a novel and efficient attention-based multi-exposure HDR imaging method, which skillfully selects the optimal information (clean or sharp) from corrupted LDR inputs by our customized cross-attention mechanism to generate HDR information. Furthermore, to enhance the robustness of our cross-attention mechanism, we introduce a novel <b>E</b>ntropy <b>D</b>ecreasing loss (ED loss) which decreases the entropy of the calculated attention map to alleviate the ghosting artifacts during multi-exposure information fusion. Extensive experimental results demonstrate that the proposed method trained on the proposed dataset surpasses related state-of-the-art methods with outstanding real-world photography quality. Codes and the dataset will be available.</p>