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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\n                    <jats:bold>E<\/jats:bold>\n                    ntropy\n                    <jats:bold>D<\/jats:bold>\n                    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.\n                  <\/jats:p>","DOI":"10.1007\/s11263-025-02537-w","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T18:48:15Z","timestamp":1754333295000},"page":"7536-7552","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing HDR Imaging with Joint Denoising and Deblurring"],"prefix":"10.1007","volume":"133","author":[{"given":"Qiang","family":"Wen","sequence":"first","affiliation":[]},{"given":"Zhefan","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Chenyang","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Wenxiu","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Qiong","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Lei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2199-3948","authenticated-orcid":false,"given":"Qifeng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,4]]},"reference":[{"key":"2537_CR1","doi-asserted-by":"crossref","unstructured":"A Sharif, S., Naqvi, R. 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