Light field imaging, which captures spatial-angular information of light incident on image sensors, enables many interesting applications such as image refocusing and augmented reality. However, due to the limited sensor resolution, a trade-off exists between the spatial and angular resolutions. To increase the angular resolution, view synthesis techniques have been adopted to generate new views from existing views. However, traditional learning-based view synthesis mainly considers the image quality of each view of the light field and neglects the quality of the refocused images. In this paper, we propose a new loss function called refocused image error (RIE) to address the issue. The main idea is that the image quality of the synthesized light field should be optimized in the refocused image domain because it is where the light field is viewed. We analyze the behavior of RIE in the spectral domain and test the performance of our approach against previous approaches on both real (INRIA) and software-rendered (HCI) light field datasets using objective assessment metrics such as MSE, MAE, PSNR, SSIM, and GMSD. Experimental results show that the light field generated by our method results in better refocused images than previous methods.

中文翻译：

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通过在重新聚焦图像域中训练深度网络进行光场合成。


光场成像可捕获入射到图像传感器上的光的空间角度信息，可实现许多有趣的应用，例如图像重新聚焦和增强现实。然而，由于传感器分辨率有限，空间分辨率和角度分辨率之间存在权衡。为了提高角分辨率，已采用视图合成技术从现有视图生成新视图。然而，传统的基于学习的视图合成主要考虑光场每个视图的图像质量，而忽略了重聚焦图像的质量。在本文中，我们提出了一种新的损失函数，称为重新聚焦图像误差（RIE）来解决这个问题。主要思想是，合成光场的图像质量应该在重聚焦图像域中进行优化，因为这是观察光场的地方。我们分析了 RIE 在光谱域中的行为，并使用 MSE、MAE、PSNR、SSIM 等客观评估指标，在真实 (INRIA) 和软件渲染 (HCI) 光场数据集上测试我们的方法与之前方法的性能，和 GMSD。实验结果表明，我们的方法生成的光场比以前的方法产生更好的重聚焦图像。