Light Field Reconstruction Based on Compressed Sensing via Deep Learning The light field has excellent application prospects in immersive media because of the abundant information of the light. Due to the sparsity and redundancy in light field images, light field reconstruction based on compressed sensing is used to recover light field images from only a few measurements. And the light field compressed sensing usually optimizes the measurement matrix and the dictionary and processes each of the light field images separately. Since the high similarity of light field images, the different viewpoints of images can be stacked together and formed as a 4D tensor. In this paper, we propose tensor based on compressed sensing (TCS) method to yield measurements with common characteristics. Besides, a better deep learning network is designed for TCS, the measurement matrix optimization and image reconstruction will be performed simultaneously. Experimental results show that the proposed method gets at least 3 dB gain in PSNR and outperforms stateoftheart in the reconstruction quality.

DepthGuided View Synthesis for Light Field Reconstruction From a Single Image Abstract   Light field imaging has recently become a promising technology for 3D rendering and displaying. However, capturing realworld light field images still faces many challenges in both the quantity and quality. In this paper, we develop a learning based technique to reconstruct light field from a single 2D RGB image. It includes three steps: unsupervised monocular depth estimation, view synthesis and depthguided view inpainting. We first propose a novel monocular depth estimation network to predict disparity maps of each subaperture views from the central view of light field. Then we synthesize the initial subaperture views by using the warping scheme. Considering that occlusion makes synthesis ambiguous for pixels invisible in the central view, we present a simple but effective fully convolutional network (FCN) for view inpainting. Note that the proposed network architecture is a general framework for light field reconstruction, which can be extended to take a sparse set of views as input without changing any structure or parameters of the network. Comparison experiments demonstrate that our method outperforms the stateoftheart light field reconstruction methods with singleview input, and achieves comparable results with the multiinput methods.