Abstract: Reconstructing high dynamic range (HDR) images from multiple low dynamic range (LDR) images with different exposures is a challenging task, especially when camera and object motion are present. In such cases, motion regions often introduce artifacts that degrade the quality of the final reconstructed image. The root cause of this issue lies in the poor alignment of content across the multiple LDR images, where geometric discrepancies between the images significantly affect the reconstruction results. To address this problem, this paper proposes a feature pre-alignment-based HDR image reconstruction network, designed to improve HDR reconstruction through the pre-alignment of features. The network consists of two main components: the feature pre-alignment module and the HDR reconstruction module. In the feature pre-alignment module, a feature alignment network is introduced, which guides the alignment of the input image features with those of a reference image, thereby reducing motion-induced artifacts. The reconstruction module models the global context of the pre-aligned features using a selective state-space model and generates the final HDR image via a simplified HDR recovery network. To evaluate the performance of the proposed network, extensive experiments were conducted on the Kalantari dataset. The results show that the network outperforms existing methods across multiple objective metrics and demonstrates superior subjective visual quality. Furthermore, to validate the generalization capability of the network, the proposed model was trained on the Kalantari training set and subsequently tested on the Sen dataset. The results indicate that the proposed network exhibits a certain degree of generalization ability.

摘要： 基于多张不同曝光的低动态范围（LDR）图像重建高动态范围（HDR）图像是一项具有挑战性的任务，尤其是在存在相机和物体运动的情况下，运动区域常常会产生伪影，从而影响最终重建图像的质量。这一问题的根本原因在于多张LDR图像在内容上的对齐不佳，图像之间的几何差异会显著影响重建效果。为了解决这一问题，本文提出了一种特征预对齐的高动态范围图像重建网络，该网络旨在通过特征的预对齐来提高HDR重建的效果。该网络由两个主要部分组成：特征预对齐部分和HDR重建部分。在特征预对齐部分，设计了一个特征对齐网络，该网络能够引导输入图像的特征与参考图像对齐，从而减少运动引起的伪影。重建部分通过选择性状态空间模型来对预对齐后的特征进行全局上下文的建模，并通过简化的HDR恢复网络生成最终的HDR图像。为了评估所提网络的性能，本文在Kalantari数据集上进行了广泛的实验，实验结果显示，该网络在多个客观指标上优于现有方法，并在主观视觉感受上也呈现出不错效果。另外，为验证该网络模型的泛化能力，在Kalantari数据集的训练集上训练了所提出的网络，并随后将该训练好的网络在Sen的数据集上进行测试，结果表明本文所提网络具有一定的泛化能力。