Salem Ahmed, Ibrahem Hatem, Kang Hyun-Soo
School of Information and Communication Engineering, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea.
Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71515, Egypt.
Sensors (Basel). 2022 Jul 14;22(14):5254. doi: 10.3390/s22145254.
Dense multi-view image reconstruction has played an active role in research for a long time and interest has recently increased. Multi-view images can solve many problems and enhance the efficiency of many applications. This paper presents a more specific solution for reconstructing high-density light field (LF) images. We present this solution for images captured by Lytro Illum cameras to solve the implicit problem related to the discrepancy between angular and spatial resolution resulting from poor sensor resolution. We introduce the residual channel attention light field (RCA-LF) structure to solve different LF reconstruction tasks. In our approach, view images are grouped in one stack where epipolar information is available. We use 2D convolution layers to process and extract features from the stacked view images. Our method adopts the channel attention mechanism to learn the relation between different views and give higher weight to the most important features, restoring more texture details. Finally, experimental results indicate that the proposed model outperforms earlier state-of-the-art methods for visual and numerical evaluation.
长期以来，密集多视图图像重建在研究中发挥了积极作用，且最近受到的关注有所增加。多视图图像可以解决许多问题并提高许多应用的效率。本文提出了一种用于重建高密度光场（LF）图像的更具体解决方案。我们针对Lytro Illum相机拍摄的图像提出此解决方案，以解决因传感器分辨率差而导致的角度分辨率和空间分辨率之间差异的隐含问题。我们引入残差通道注意力光场（RCA-LF）结构来解决不同的LF重建任务。在我们的方法中，视图图像被分组在一个堆栈中，其中存在对极信息。我们使用二维卷积层来处理并从堆叠的视图图像中提取特征。我们的方法采用通道注意力机制来学习不同视图之间的关系，并对最重要的特征赋予更高的权重，从而恢复更多的纹理细节。最后，实验结果表明，所提出的模型在视觉和数值评估方面优于早期的最先进方法。
