论文标题
通过混合CNN转换器和结构张量的无幽灵高动态范围成像
Ghost-free High Dynamic Range Imaging via Hybrid CNN-Transformer and Structure Tensor
论文作者
论文摘要
在高动态范围（HDR）成像中，消除由于移动物体而导致的幽灵伪影是一个具有挑战性的问题。在这封信中，我们提出了一个由卷积编码器和变压器解码器组成的混合模型，以生成无幽灵的HDR图像。在编码器中，采用了上下文聚合网络和非本地注意块来优化多尺度功能并捕获多个低动态范围（LDR）图像的全球和本地依赖性。基于SWIN变压器的解码器用于提高所提出模型的重建能力。由结构张量（ST）下的伪影之间存在与不存在之间的显着差异的动机，我们将LDR图像的ST信息整合为网络的辅助输入，并使用ST损失来进一步限制文物。与以前的方法不同，我们的网络能够处理任意数量的输入LDR图像。定性和定量实验通过将其与现有最新的HDR脱胶模型进行比较，证明了该方法的有效性。代码可在https://github.com/pandayuanyu/hsthdr上找到。
Eliminating ghosting artifacts due to moving objects is a challenging problem in high dynamic range (HDR) imaging. In this letter, we present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR images. In the encoder, a context aggregation network and non-local attention block are adopted to optimize multi-scale features and capture both global and local dependencies of multiple low dynamic range (LDR) images. The decoder based on Swin Transformer is utilized to improve the reconstruction capability of the proposed model. Motivated by the phenomenal difference between the presence and absence of artifacts under the field of structure tensor (ST), we integrate the ST information of LDR images as auxiliary inputs of the network and use ST loss to further constrain artifacts. Different from previous approaches, our network is capable of processing an arbitrary number of input LDR images. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed method by comparing it with existing state-of-the-art HDR deghosting models. Codes are available at https://github.com/pandayuanyu/HSTHdr.
