The Image-Based Rendering (IBR) approach using Shearlet Transform (ST) is one of the most effective methods for Densely-Sampled Light Field (DSLF) reconstruction. The ST-based DSLF reconstruction typically relies on an iterative thresholding algorithm for Epipolar-Plane Image (EPI) sparse regularization in shearlet domain, involving dozens of transformations between image domain and shearlet domain, which are in general time-consuming. To overcome this limitation, a novel learning-based ST approach, referred to as Deep Residual Shearlet Transform (DRST), is proposed in this paper. Specifically, for an input sparsely-sampled EPI, DRST employs a deep fully Convolutional Neural Network (CNN) to predict the residuals of the shearlet coefficients in shearlet domain in order to reconstruct a densely-sampled EPI in image domain. The DRST network is trained on synthetic Sparsely-Sampled Light Field (SSLF) data only by leveraging elaborately-designed masks. Experimental results on three challenging real-world light field evaluation datasets with varying moderate disparity ranges (8 - 16 pixels) demonstrate the superiority of the proposed learning-based DRST approach over the non-learning-based ST method for DSLF reconstruction. Moreover, DRST provides a 2.4x speedup over ST, at least.

中文翻译：

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DRST：用于密集采样光场重建的深度残差剪切波变换

使用剪切波变换 (ST) 的基于图像的渲染 (IBR) 方法是用于密集采样光场 (DSLF) 重建的最有效方法之一。基于 ST 的 DSLF 重建通常依赖于剪切波域中对极平面图像 (EPI) 稀疏正则化的迭代阈值算法，涉及图像域和剪切波域之间的数十次转换，通常很耗时。为了克服这一限制，本文提出了一种新的基于学习的 ST 方法，称为 Deep Residual Shearlet Transform (DRST)。具体来说，对于输入稀疏采样的 EPI，DRST 采用深度全卷积神经网络 (CNN) 来预测剪切域中剪切波系数的残差，以重建图像域中的密集采样 EPI。DRST 网络仅通过利用精心设计的掩码对合成的稀疏采样光场 (SSLF) 数据进行训练。在具有不同中等视差范围（8 - 16 像素）的三个具有挑战性的真实世界光场评估数据集上的实验结果表明，所提出的基于学习的 DRST 方法优于基于非学习的 ST 方法进行 DSLF 重建。此外，DRST 至少比 ST 提供 2.4 倍的加速。