With the availability of commercial Light Field (LF) cameras, LF imaging has emerged as an up and coming technology in computational photography. However, the spatial resolution is significantly constrained in commercial microlens based LF cameras because of the inherent multiplexing of spatial and angular information. Therefore, it becomes the main bottleneck for other applications of light field cameras. This paper proposes an adaptation module in a pretrained Single Image Super Resolution (SISR) network to leverage the powerful SISR model instead of using highly engineered light field imaging domain specific Super Resolution models. The adaption module consists of a Sub aperture Shift block and a fusion block. It is an adaptation in the SISR network to further exploit the spatial and angular information in LF images to improve the super resolution performance. Experimental validation shows that the proposed method outperforms existing light field super resolution algorithms. It also achieves PSNR gains of more than 1 dB across all the datasets as compared to the same pretrained SISR models for scale factor 2, and PSNR gains 0.6 to 1 dB for scale factor 4.
翻译:随着商业光场照相机(LF)的可用性,LF成像在计算摄影中已成为一种上流和即将出现的技术,然而,由于空间和角信息的内在多重作用,空间分辨率在以商业微粒为基础的LF照相机中受到很大限制,因此它成为光场照相机其他应用的主要瓶颈。本文件提议在预先培训的单一图像超级分辨率网络中采用一个适应模块,以利用强大的SISSR模型,而不是使用高设计光场成像域域特定超级分辨率模型。适应模块包括一个次级孔径转换块和一个聚变块。这是对SISR网络的调整,以进一步利用LF图像中的空间和角信息来改进超分辨率性能。实验性论证表明,拟议的方法比现有的光场超分辨率算法更符合现有的光场超分辨率算法。它还实现了PSNR在所有数据集中超过1 dB的收益,而与规模因子2而经过预先培训的SISSR模型相比,PSNR获得0.6至1 dB的比例系数4。