The major challenge in high dynamic range (HDR) imaging for dynamic scenes is suppressing ghosting artifacts caused by large object motions or poor exposures. Whereas recent deep learning-based approaches have shown significant synthesis performance, interpretation and analysis of their behaviors are difficult and their performance is affected by the diversity of training data. In contrast, traditional model-based approaches yield inferior synthesis performance to learning-based algorithms despite their theoretical thoroughness. In this paper, we propose an algorithm unrolling approach to ghost-free HDR image synthesis algorithm that unrolls an iterative low-rank tensor completion algorithm into deep neural networks to take advantage of the merits of both learning- and model-based approaches while overcoming their weaknesses. First, we formulate ghost-free HDR image synthesis as a low-rank tensor completion problem by assuming the low-rank structure of the tensor constructed from low dynamic range (LDR) images and linear dependency among LDR images. We also define two regularization functions to compensate for modeling inaccuracy by extracting hidden model information. Then, we solve the problem efficiently using an iterative optimization algorithm by reformulating it into a series of subproblems. Finally, we unroll the iterative algorithm into a series of blocks corresponding to each iteration, in which the optimization variables are updated by rigorous closed-form solutions and the regularizers are updated by learned deep neural networks. Experimental results on different datasets show that the proposed algorithm provides better HDR image synthesis performance with superior robustness compared with state-of-the-art algorithms, while using significantly fewer training samples.

中文翻译：

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用于高动态范围成像的深度展开低秩张量补全

动态场景的高动态范围 (HDR) 成像的主要挑战是抑制由大物体运动或曝光不良引起的重影伪影。尽管最近基于深度学习的方法已显示出显着的综合性能，但对其行为的解释和分析却很困难，并且其性能受到训练数据多样性的影响。相比之下，传统的基于模型的方法产生的综合性能不如基于学习的算法，尽管它们的理论彻底性。在本文中，我们提出了一种无重影 HDR 图像合成算法的算法展开方法，该算法将迭代低秩张量完成算法展开到深度神经网络中，以利用基于学习和基于模型的方法的优点，同时克服它们的缺点。弱点。第一的，我们通过假设由低动态范围 (LDR) 图像和 LDR 图像之间的线性相关性构成的张量的低秩结构，将无重影 HDR 图像合成公式化为低秩张量补全问题。我们还定义了两个正则化函数，通过提取隐藏的模型信息来补偿建模的不准确性。然后，我们使用迭代优化算法通过将其重新构造为一系列子问题来有效地解决问题。最后，我们将迭代算法展开为与每次迭代相对应的一系列块，其中优化变量由严格的封闭形式解决方案更新，正则化器由学习的深度神经网络更新。