Prex-Net: Progressive Exploration Network Using Efficient Channel Fusion for Light Field Reconstruction
Dong‐Myung Kim, Young-Suk Yoon, Yuseok Ban, Jae‐Won Suh · Electronics · 2023
Light field (LF) reconstruction is a technique for synthesizing views between LF images and various methods have been proposed to obtain high-quality LF reconstructed images. In this paper, we propose a progressive exploration network using efficient channel fusion for light field reconstruction (Prex-Net), which consists of three parts to quickly produce high-quality synthesized LF images. The initial feature extraction module uses 3D convolution to obtain deep correlations between multiple LF input images. In the channel fusion module, the extracted initial feature map passes through successive up- and down-fusion blocks and continuously searches for features required for LF reconstruction. The fusion block collects the pixels of channels by pixel shuffle and applies convolution to the collected pixels to fuse the information existing between channels. Finally, the LF restoration module synthesizes LF images with high angular resolution through simple convolution using the concatenated outputs of down-fusion blocks. The proposed Prex-Net synthesizes views between LF images faster than existing LF restoration methods and shows good results in the PSNR performance of the synthesized image.