基于Shearlet变换与循环一致性的自监督光场重建
Self-Supervised Light Field Reconstruction Using Shearlet Transform and Cycle Consistency
AI总结：
针对传统Shearlet变换光场重建速度慢的问题，提出结合Shearlet变换与循环一致性的自监督重建方法CycleST，仅需小视差疏采样光场训练，在大视差场景下实现更优性能且速度至少提升9倍。
摘要：
基于Shearlet变换（ST）的图像渲染方法是当前最先进的密采样光场（DSLF）重建方法之一。该方法通过迭代正则化算法在shearlet域恢复系数，进而在图像域重建极线平面图像（EPI）。但由于数十次迭代的域变换耗时较长，ST方法往往运行速度较慢。为解决这一局限，本文提出一种新型自监督DSLF重建方法CycleST，将ST与循环一致性应用于DSLF重建任务。具体而言，CycleST由编码器-解码器网络和残差学习策略构成，借助EPI重建损失与循环一致性损失恢复密采样EPI的shearlet系数。此外，CycleST是一种自监督方法，仅需在视差范围较小（≤8像素）的疏采样光场（SSLF）上训练即可。在两个具有挑战性的真实世界光场数据集上，针对大视差范围（16-32像素）的SSLF开展DSLF重建实验，结果验证了所提CycleST方法的有效性与高效性。此外，CycleST的速度相比ST至少提升了约9倍。
英文摘要：
The image-based rendering approach using Shearlet Transform (ST) is one of the state-of-the-art Densely-Sampled Light Field (DSLF) reconstruction methods. It reconstructs Epipolar-Plane Images (EPIs) in image domain via an iterative regularization algorithm restoring their coefficients in shearlet domain. Consequently, the ST method tends to be slow because of the time spent on domain transformations for dozens of iterations. To overcome this limitation, this letter proposes a novel self-supervised DSLF reconstruction method, CycleST, which applies ST and cycle consistency to DSLF reconstruction. Specifically, CycleST is composed of an encoder-decoder network and a residual learning strategy that restore the shearlet coefficients of densely-sampled EPIs using EPI reconstruction and cycle consistency losses. Besides, CycleST is a self-supervised approach that can be trained solely on Sparsely-Sampled Light Fields (SSLFs) with small disparity ranges ($\leqslant$ 8 pixels). Experimental results of DSLF reconstruction on SSLFs with large disparity ranges (16 - 32 pixels) from two challenging real-world light field datasets demonstrate the effectiveness and efficiency of the proposed CycleST method. Furthermore, CycleST achieves ~ 9x speedup over ST, at least.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
