High Dynamic Range (HDR) imaging aims to generate an artifact-free HDR image with realistic details by fusing multi-exposure Low Dynamic Range (LDR) images. Caused by large motion and severe under-/over-exposure among input LDR images, HDR imaging suffers from ghosting artifacts and fusion distortions. To address these critical issues, we propose an HDR Transformer Deformation Convolution (HDRTransDC) network to generate high-quality HDR images, which consists of the Transformer Deformable Convolution Alignment Module (TDCAM) and the Dynamic Weight Fusion Block (DWFB). To solve the ghosting artifacts, the proposed TDCAM extracts long-distance content similar to the reference feature in the entire non-reference features, which can accurately remove misalignment and fill the content occluded by moving objects. For the purpose of eliminating fusion distortions, we propose DWFB to spatially adaptively select useful information across frames to effectively fuse multi-exposed features. Extensive experiments show that our method quantitatively and qualitatively achieves state-of-the-art performance.

翻译：高动态范围（HDR）成像旨在通过融合多曝光低动态范围（LDR）图像，生成无伪影且细节逼真的HDR图像。由于输入LDR图像中存在大范围运动及严重过曝/欠曝现象，HDR成像易产生鬼影伪影和融合失真问题。为解决这些关键问题，我们提出一种HDR Transformer形变卷积网络（HDRTransDC），通过构建Transformer形变卷积对齐模块（TDCAM）与动态权重融合块（DWFB）生成高质量HDR图像。针对鬼影伪影，所提出的TDCAM可在整个非参考特征中提取与参考特征相似的长距离内容，精准消除未对齐区域并填充被运动物体遮挡的内容；为消除融合失真，我们提出DWFB在空间上自适应选择跨帧有用信息，有效融合多曝光特征。大量实验表明，本方法在定量与定性指标上均达到最优性能。