In this paper, we propose a novel filtering method based on deep attention networks for the quality enhancement of light field (LF) images captured by plenoptic cameras and compressed using the High Efficiency Video Coding (HEVC) standard. The proposed architecture was built using efficient complex processing blocks and novel attention-based residual blocks. The network takes advantage of the macro-pixel (MP) structure, specific to LF images, and processes each reconstructed MP in the luminance (Y) channel. The input patch is represented as a tensor that collects, from an MP neighbourhood, four Epipolar Plane Images (EPIs) at four different angles. The experimental results on a common LF image database showed high improvements over HEVC in terms of the structural similarity index (SSIM), with an average Y-Bjøntegaard Delta (BD)-rate savings of $36.57\%,$ and an average Y-BD-PSNR improvement of $2.301$ dB. Increased performance was achieved when the HEVC built-in filtering methods were skipped. The visual results illustrate that the enhanced image contains sharper edges and more texture details. The ablation study provides two robust solutions to reduce the inference time by $44.6\%$ and the network complexity by $74.7\%$. The results demonstrate the potential of attention networks for the quality enhancement of LF images encoded by HEVC.

中文翻译：

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增强网络以增强光场图像的质量

在本文中，我们提出了一种基于深度关注网络的新型滤波方法，用于增强由全光摄像机捕获并使用高效视频编码（HEVC）标准压缩的光场（LF）图像的质量。所提出的架构是使用高效的复杂处理模块和新颖的基于注意力的残差模块构建的。该网络利用了专用于LF图像的宏像素（MP）结构，并在亮度（Y）通道中处理每个重建的MP。输入面片表示为张量，该张量从MP邻域收集四个不同角度的四个对极平面图像（EPI）。在普通的LF图像数据库上的实验结果表明，相对于HEVC，在结构相似性指数（SSIM）方面有很大改进，平均Y-BjøntegaardDelta（BD）节省了$36.57％，$ 并且Y-BD-PSNR的平均改善为 $2.301$D b。跳过HEVC内置过滤方法，可以提高性能。视觉结果表明，增强后的图像包含更清晰的边缘和更多的纹理细节。消融研究提供了两种可靠的解决方案，以减少推理时间：$44.6％$ 和网络的复杂性 $74.7％$。结果证明了注意力网络在增强HEVC编码的LF图像质量方面的潜力。