Realtime Compressive Video Reconstruction for Spatial Multiplexing Cameras In this work, we tackle the problem of realtime compressive video reconstruction in spatial multiplexing cameras using spatially modulated and undersampled focal plane array data. In this setting, a spatial light modulator (SLM) modulates the scene in the image domain by blocking some of the pixels at a higher resolution level, using an SLM such as a digital micromirror device. Here, we first propose a practical warmstarting method to achieve realtime reconstruction of streaming video. We then extend it by adapting a multihypothesis technique that takes temporal consistency into account using residual reconstruction between frames. We implement the algorithms on a graphics processing unit and give an extensive analysis of the number of required iterations for different imaging settings. We conclude that the proposed methods achieve video reconstruction in realtime from highly undersampled compressed measurements and provide high quality frames in terms of PSNR and SSIM metrics.

Learning Based Regularization for Spatial Multiplexing Cameras In this paper, we consider learning based regularization for compressive sensing reconstruction using focal plane array sensors. While many optimization algorithms employ proximal operators for regularization, they are often inadequate in fully capturing the characteristics of complex natural images. Recently, deep learning based approaches obtained promising results in different imaging problems, creating the possibility to use them for regularization in an optimization framework. Here, we utilize this approach in compressive sensing based spatial multiplexing cameras. This technique is motivated by the high cost of producing large focal plane arrays in infrared sensors. Reconstruction from undersampled measurements can be done using a spatial multiplexing camera which relies on multiple snapshots for superresolving a scene. It acquires coded projections of a scene using a spatial light modulator and a lowresolution focal plane array. We first formulate the problem of finding a high resolution image from its undersampled measurements. Then, we develop a reconstruction method with learning based regularization to solve this problem using alternating direction method of multipliers framework. For this, we replace the proximal operator corresponding to the regularization function with a deep convolutional denoising network. We also enhance a previously proposed denoising networkxe2x80x99s training phase by introducing multiple noise realizations for each training patch, which results in better reconstruction performance. Numerical results for different imaging scenarios show successful recovery of high resolution images in terms of PSNR, SSIM and visual quality at significant noise levels.