Adaptive framework for robust high-resolution image reconstruction in multiplexed computational imaging architectures

Abstract

In multiplexed computational imaging schemes, high-resolution images are reconstructed by fusing the information in multiple low-resolution images detected by a two-dimensional array of low-resolution image sensors. The reconstruction procedure assumes a mathematical model for the imaging process that could have generated the low-resolution observations from an unknown high-resolution image. In practical settings, the parameters of the mathematical imaging model are known only approximately and are typically estimated before the reconstruction procedure takes place. Violations to the assumed model, such as inaccurate knowledge of the field of view of the imagers, erroneous estimation of the model parameters, and/or accidental scene or environmental changes can be detrimental to the reconstruction quality, even if they are small in number. We present an adaptive algorithm for robust reconstruction of high-resolution images in multiplexed computational imaging architectures. Using robust M-estimators and incorporating a similarity measure, the proposed scheme adopts an adaptive estimation strategy that effectively deals with violations to the assumed imaging model. Comparisons with nonadaptive reconstruction techniques demonstrate the superior performance of the proposed algorithm in terms of reconstruction quality and robustness.

Super Resolution results in PANOPTES, an adaptive multi-aperture folded architecture

“…In matrix notation, (1) can be written as: (2) where is the input image in lexicographical notation resulting in a vector of size , and are kth warping and blurring operators of size , is the downsampling matrix of size , and and are the vectors of kth LR image and noise respectively, both of size . Typically and are the same for all LR images.…”

“…Typically and are the same for all LR images. Alternatively, the model in (2) can be expressed in terms of the entire set of LR images as:…”

“…In [2] El-Yamani and Papamichalis suggested applying the Lorentzian error norm to better suppress the effect of large errors in the cost function. We provide a comparison of reconstruction from selected methods in Section 5.…”

“…In recent years there has been a big trend toward generating high resolution (HR) images/videos by fusing information from a series of low resolution (LR) image/video frames. This trend has activities in two main research areas, multiplexed computational imaging (MCI) and digital super resolution (DSR) [2]. MCI primarily focuses on joint design of optics, detector and reconstruction algorithm.…”