Microlens Array Grid Estimation, Light Field Decoding, and Calibration We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens arraybased light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing applicationspecific raytraced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignettingaware grid estimation, especially in peripheral subapertures of the light field.

FrameWise CNNBased View Synthesis for Light Field Camera Arrays The paper proposes a novel framewise view synthesis method based on convolutional neural networks (CNNs) for widebaseline light field (LF) camera arrays. A novel neural network architecture that follows a multiresolution processing paradigm is employed to synthesize an entire view. A novel loss function formulation based on the structural similarity index (SSIM) is proposed. A widebaseline LF image dataset is generated and employed to train the proposed deep model. The proposed method synthesizes each subaperture image (SAI) from a LF image based on corresponding SAIs from two reference LF images. Experimental results show that the proposed method yields promising results with an average PSNR and SSIM of 34.71 dB and 0.9673 respectively for wide baselines.