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Light fields present a rich way to represent the 3D world by capturing the spatio-angular dimensions of the visual signal. However, the popular way of capturing light fields (LF) via a plenoptic camera presents a spatio-angular resolution tradeoff. Computational imaging techniques such as compressive light field and programmable coded aperture reconstruct full sensor resolution LF from coded projections obtained by multiplexing the incoming spatio-angular light field. Here, we present a unified learning framework that can reconstruct LF from a variety of multiplexing schemes with minimal number of coded images as input. We consider three light field capture schemes: heterodyne capture scheme with code placed near the sensor, coded aperture scheme with code at the camera aperture and finally the dual exposure scheme of capturing a focus-defocus pair where there is no explicit coding. Our algorithm consists of three stages 1) we recover the all-in-focus image from the coded image 2) we estimate the disparity maps for all the LF views from the coded image and the all-in-focus image, 3) we then render the LF by warping the all-in-focus image using disparity maps. We show that our proposed learning algorithm performs either on par with or better than the state-of-the-art methods for all the three multiplexing schemes. LF from focus-defocus pair is especially attractive as it requires no hardware modification and produces LF reconstructions that are comparable to the current state of the art learning-based view synthesis approaches from multiple images. Thus, our work paves the way for capturing full-resolution LF using conventional cameras such as DSLRs and smartphones.Index Terms-Light field resolution trade-off, compressive light field imaging, coded aperture photography, disparity based view synthesis.

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Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep networks that are computationally expensive and have huge memory footprints. This makes it exceedingly difficult to deploy these systems on low power embedded devices. Hence, the importance of decreasing the storage requirements and the amount of computation in such models is paramount. The recently proposed Lottery Ticket Hypothesis (LTH) states that deep neural networks trained on large datasets contain smaller subnetworks that achieve on par performance as the dense networks. In this work, we perform the first empirical study investigating LTH for model pruning in the context of object detection, instance segmentation, and keypoint estimation. Our studies reveal that lottery tickets obtained from Imagenet pretraining do not transfer well to the downstream tasks. We provide guidance on how to find lottery tickets with up to 80% overall sparsity on different sub-tasks without incurring any drop in the performance. Finally, we analyse the behavior of trained tickets with respect to various task attributes such as object size, frequency, and difficulty of detection.

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