Abstract
Deep high dynamic range (HDR) imaging as an image translation issue has achieved great performance without explicit optical flow alignment. However, challenges remain over content association ambiguities especially caused by saturation and large-scale movements. To address the ghosting issue and enhance the details in saturated regions, we propose a scale-aware two-stage high dynamic range imaging framework (STHDR) to generate high-quality ghost-free HDR image. The scale-aware technique and two-stage fusion strategy can progressively and effectively improve the HDR composition performance. Specifically, our framework consists of feature alignment and two-stage fusion. In feature alignment, we propose a spatial correct module (SCM) to better exploit useful information among non-aligned features to avoid ghosting and saturation. In the first stage of feature fusion, we obtain a preliminary fusion result with little ghosting. In the second stage, we conflate the results of the first stage with aligned features to further reduce residual artifacts and thus improve the overall quality. Extensive experimental results on the typical test dataset validate the effectiveness of the proposed STHDR in terms of speed and quality.
Abstract (translated)
Deep high dynamic range (HDR) imaging作为一种图像转换问题,已经取得了出色的表现,但缺乏明确的光学流对齐。然而,仍有关于内容匹配混淆的挑战,特别是由于饱和度和大规模运动引起的。为了解决渲染问题和提高饱和度区域的细节,我们提出了一种 Scale-aware 两阶段高动态范围成像框架(STHDR),以生成高质量的无渲染鬼影的HDR图像。Scale-aware技术和两阶段融合策略可以逐步有效地改进HDR组合性能。具体来说,我们的框架包括特征对齐和两阶段融合。在特征对齐中,我们提出了一个空间正确模块(SCM),更好地利用非对齐特征之间的有用信息,避免渲染和饱和度。在特征融合的第一阶段,我们获得几乎没有渲染的初步融合结果。在第二阶段,我们将第一阶段的结果与对齐特征混淆,进一步减少残留 artifacts,从而提高整体质量。对典型的测试数据集广泛的实验结果验证了提出的STHDR在速度和质量方面的有效性。
URL
https://arxiv.org/abs/2303.06575
